The DOI matches its Crossref record; 74 of the 81 references it cites were found in a registry or an index; 2 of the 26 sources its claims cite were checked for support.
Learning Deep CNN Denoiser Prior for Image Restoration
Open accessAbstract
Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance, in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the study of integration with fast discriminative denoiser prior is still lacking. To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers can not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper trains a set of fast CNN Gaussian denoisers and uses them as modular priors inside a half-quadratic splitting optimization framework to solve image deblurring and super-resolution. Experiments show competitive denoising accuracy and flexible restoration performance across tasks without task-specific retraining.
Key findings
- The proposed CNN denoiser improves gray-image denoising on BSD68 over BM3D, WNNM, TNRD, and MLP: at σ=15 it reaches 31.63 dB, at σ=25 it reaches 29.15 dB, and at σ=50 it reaches 26.19 dB.
- For color denoising on BSD68, the proposed denoiser consistently beats CBM3D, e.g. 40.36 vs. 40.24 dB at noise level 5, 31.16 vs. 30.71 dB at 25, and 27.86 vs. 27.38 dB at 50.
- The learned denoisers can be inserted into a model-based optimization method to handle deblurring and single-image super-resolution; for example, on the Leaves deblurring case with Gaussian blur σ=1.6 and noise level 2, the proposed method reports 29.78 dB versus 26.95 dB for IDDBM3D and 27.50 dB for NCSR.
- Runtime is competitive: for a 256×256 image with noise level 25, the 24-feature-map version takes 0.10 s on CPU and 0.006 s on GPU, while BM3D takes 0.66 s on CPU and TNRD takes 0.47 s on CPU / 0.010 s on GPU.
Methods
- The authors design a seven-layer CNN denoiser using dilated convolutions with dilation factors 1, 2, 3, 4, 3, 2, 1; middle layers use 64 feature maps, and the effective receptive field is 33×33.
- Training uses 400 BSD images, 400 ImageNet validation images, and 4,744 Waterloo Exploration Database images, cropped into 35×35 patches with N = 256×4,000 patches; Adam is used with residual learning and batch normalization.
- A set of 25 denoisers is trained for gray images and 25 for color images over noise levels from 0 to 50 in steps of 2; these denoisers are integrated into a half-quadratic splitting scheme, where the regularization subproblem becomes a Gaussian denoising step.
Limitations
- The paper states that future work includes reducing the number of CNN denoisers and iterations, extending the framework to inpainting and blind deblurring, and using multiple complementary priors.
- The experimental evaluation is limited to denoising, non-blind deblurring, and super-resolution; inpainting and blind deblurring are not tested.
- The authors note that comparisons with SRCNN and VDSR for non-bicubic super-resolution are unfair because those methods were trained for bicubic degradation, which is evident from the reported setup rather than presented as a limitation of the proposed method.
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Claims the paper makes
2 of 26 cited sources checked · 1 supported · 1 flagged · accuracy not yet measured
At least one cited source doesn’t support this claim or points to a different paper, and none confirmed it — see the verdicts below.
In the paper
“Note that SRCNN and VDSR are trained with bicubic blur kernel, thus it is unfair to use their models to super-resolve the low-resolution image with Gaussian kernel.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.3. Single Image Super-Resolution
What the sources say
- Disputedcites [21] 21. C. Dong, C. C. Loy, K. He, and X. Tang. Image super-resolution using deep convolutional networks. IEEE transactions on Pattern Analysis and Machine Intelligence, 38(2):295-307, 2016. 2, 7 sourceExperimental — accuracy not yet measured
Section 3.3 (p. 5) says SRCNN training samples are blurred with a Gaussian kernel; bicubic interpolation is a later upscaling step, not the blur kernel. Section 4.4.1 (p. 9) also describes training a separate SRCNN network with a different Gaussian kernel for comparison. The paper does not address VDSR's training. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
- Not checked — no full text of the sourcecites [35] 35. J. Kim, J. K. Lee, and K. M. Lee. Accurate image super- resolution using very deep convolutional networks. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1646-1654, 2016. 4, 7 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Disputed
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Experimental results demonstrate that the learned set of denoisers can not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.”
- Also stated in: § 1. Introduction · boosted
“Extensive experiments on classical IR problems, including deblurring and super-resolution, have demonstrated the merits of integrating flexible model-based optimization methods and fast CNN-based discriminative learning methods.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § 1. Introduction · boosted
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we empirically find it also can enable fast transfer from one model to another with different noise level.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > Using Batch Normalization and Residual Learning to Accelerate Training.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, if the size of the training patch is smaller than the receptive field, the performance would decrease.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > Using Batch Normalization and Residual Learning to Accelerate Training.
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the proposed CNN denoiser can have a PSNR gain of about 0.2dB over those three methods.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.1. Image Denoising
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We empirically find that using training samples with small size can help avoid boundary artifacts.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > Using Batch Normalization and Residual Learning to Accelerate Training.
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We can see that the proposed denoiser is very competitive in both CPU and GPU implementation.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.1. Image Denoising
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Due to the iterative scheme, it turns out the noise level range of [0, 50] is enough to handle various image restoration problems.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > Using Batch Normalization and Residual Learning to Accelerate Training.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“it can be seen that the proposed denoiser consistently outperforms CBM3D by a large margin.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.1. Image Denoising
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It can be seen that WNNM, MLP and TNRD can outperform BM3D by about 0.3dB in PSNR.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.1. Image Denoising
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
- It may state two separate claims as one. 13
- Probably not a substantial claim. 10
- The quote only partly states this claim. 1
Every source the paper cites for this claim was checked and supports it.
In the paper
“it has been pointed out that the combination of batch normalization and residual learning is particularly helpful for Gaussian denoising since they are beneficial to each other.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > Using Batch Normalization and Residual Learning to Accelerate Training.
What the sources say
- Supportedcites [65] 65. K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang. Be- yond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Process- ing, 2017. 4 source
Section C (pp. 4–5) says residual learning and batch normalization can benefit each other in Gaussian denoising, and reports faster convergence and better denoising performance when combined. Section B (p. 3) also describes their integration as producing fast, stable training and better denoising performance. (LLM, conf=0.95)
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is worth emphasizing that the proposed denoiser with 24 feature maps of each layer has a comparable PSNR of 28.94dB to TNRD but delivers a faster speed.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.1. Image Denoising
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“We empirically find using large dataset does not improve the PSNR results of BSD68 dataset [50] but can slightly improve the performance of other testing images.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.1. Image Denoising
What the sources say
- Not checked — no full text of the sourcecites [50] 50. S. Roth and M. J. Black. Fields of experts. International Journal of Computer Vision, 82(2):205-229, 2009. 1, 4, 5 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“It turns out that our designed model can have an average P-SNR of 29.15dB on BSD68 dataset [50], which is much better than 28.94dB of 7 layers network with traditional 3×3 convolution filter and very close to 29.20dB of 16 layers network.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 3.2. The Proposed CNN Denoiser
What the sources say
- Not checked — no full text of the sourcecites [50] 50. S. Roth and M. J. Black. Fields of experts. International Journal of Computer Vision, 82(2):205-229, 2009. 1, 4, 5 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As an example for the testing speed comparison, our method can super-resolve the Butterfly image in 0.5 second on GPU and 12 seconds on CPU, whereas NCSR spends 198 seconds on CPU.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.3. Single Image Super-Resolution
Strength:plain· not yet calibratedSingle caseTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The low-resolution image can be modeled by a blurring and subsequent down-sampling operation on a high-resolution one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We can see that IDDBM3D, NCSR and MLP tend to smooth the edges and generate color artifacts.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.2. Image Deblurring
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The convolution is carried out with circular boundary conditions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, although S-RCNN and VDSR achieve promising results to tackle the case with bicubic kernel, their performance deteriorates seriously when the low-resolution image are not generated by bicubic kernel (see Figure 4).”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.3. Single Image Super-Resolution
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The low-resolution image can be modeled by a blurring and subsequent down-sampling operation on a high-resolution one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, the proposed method can recover image sharpness and naturalness.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.2. Image Deblurring
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The convolution is carried out with circular boundary conditions.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Third, both of the gray and color CNN denoiser prior based optimization methods can produce promising results.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.3. Single Image Super-Resolution
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The low-resolution image can be modeled by a blurring and subsequent down-sampling operation on a high-resolution one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Third, CNN exploits the external prior which is complementary to the internal prior of many existing denoisers such as BM3D.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 3.1. Why Choose CNN Denoiser?
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance”
- Also stated in: § 1. Introduction · plain
“model-based optimization methods are usually timeconsuming with sophisticated priors for the purpose of good performance [27].”
§ Learning Deep CNN Denoiser Prior for Image Restoration > Abstract
What the sources say
- Not checked — no full text of the sourcecites [27] 27. Q. Gao and S. Roth. How well do filter-based MRFs model natural images? In Joint DAGM (German Association for Pattern Recognition) and OAGM Symposium, pages 62-72, 2012. 2 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § 1. Introduction · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Second, CNN exhibits powerful prior modeling capacity with deep architecture.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 3.1. Why Choose CNN Denoiser?
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“First, the inference of CNN is very efficient due to the parallel computation ability of GPU.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 3.1. Why Choose CNN Denoiser?
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Especially noteworthy is the number of the denoisers which is much less than that of learning different models for different degradations.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > Using Batch Normalization and Residual Learning to Accelerate Training.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task.”
- Also stated in: § 1. Introduction · plain
“discriminative learning methods are usually restricted by specialized tasks.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § 1. Introduction · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such a good compromise between speed and performance over TNRD is properly attributed to the following three reasons.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.1. Image Denoising
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
In the paper
“and it is preferable to jointly handle RGB channels.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 3.1. Why Choose CNN Denoiser?
What the sources say
- Not matchedcites [42] 42. T. Miyata. Inter-channel relation based vectorial total varia- tion for color image recovery. In IEEE International Confer- ence on Image Processing,, pages 2251-2255, 2015. 3
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To make a further comparison with TNRD under similar PSNR performance, we additionally provide the run time of the proposed denoiser where each middle layer has 24 feature maps.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.1. Image Denoising
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the denoiser here just acts its own role regardless of the noise type and noise level of the image to be denoised.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > Using Batch Normalization and Residual Learning to Accelerate Training.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To show the advantage of our design over the above two cases, we have trained three different models on noise level 25 with same training settings.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 3.2. The Proposed CNN Denoiser
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is worthy noting that the iterative regularization step of methods such as NCSR and WNNM actually corresponds to solving Eqn.(6a).”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.3. Single Image Super-Resolution
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The number of iterations is set to 30 as we find it is large enough to obtain a satisfying performance.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 4.2. Image Deblurring
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- The authors state this as an assumption: The convolution is carried out with circular boundary conditions.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the other hand, different from discriminative learning methods which are specialized for certain image restoration tasks, the proposed deep CNN denoiser prior based optimization method is flexible in handling various tasks while can produce very favorable results.”
§ Learning Deep CNN Denoiser Prior for Image Restoration > 5. Conclusion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
The study of integration with fast discriminative denoiser prior is still lacking.
“However, the study of integration with fast discriminative denoiser prior is still lacking.”
“However, to the best of our knowledge, the study of integration with discriminative denoiser is still lacking.”
The authors state this as an assumption.
The convolution is carried out with circular boundary conditions.
“In addition, we assume the convolution is carried out with circular boundary conditions.”
The paper relies on this without support.
The low-resolution image can be modeled by a blurring and subsequent down-sampling operation on a high-resolution one.
“In general, the low-resolution (LR) image can be modeled by a blurring and subsequent down-sampling operation on a high-resolution one.”
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 1×
“Thus, using 3×3 filter with a large depth is popularized in existing CNN network design [30, 35, 56].”
MethodVery Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 1×
“However, increasing the filter size would not only introduce more parameters but also increase the computational burden [53].”
BackgroundImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · cited 1×
“Particularly, several CNN techniques, including Rectifier Linear Units (ReLU) [37], batch normalization [32], Adam [36], dilated convolution [63] are adopted into the network design or training.”
MethodAdam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 5×
“Particularly, several CNN techniques, including Rectifier Linear Units (ReLU) [37], batch normalization [32], Adam [36], dilated convolution [63] are adopted into the network design or training.”
MethodInfluentialImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · cited 1×
“Hence, instead of training on a small dataset consisting of 400 Berkeley segmentation dataset (BSD) images of size 180×180 [13], we collect a large dataset which includes 400 BSD images, 400 selected images from validation set of ImageNet database [20] and 4,744 images of Waterloo Exploration Database [40].”
MethodGoing deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · cited 1×
“Thus, using 3×3 filter with a large depth is popularized in existing CNN network design [30, 35, 56].”
MethodBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · cited 2×
“Particularly, several CNN techniques, including Rectifier Linear Units (ReLU) [37], batch normalization [32], Adam [36], dilated convolution [63] are adopted into the network design or training.”
BackgroundMethodBoyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
Boyd, S. (2010) · now publishers, Inc. eBooks · doi:10.1561/9781601984616 · Cited by 12,917
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Beck, A., & Teboulle, M. (2009) · SIAM Journal on Imaging Sciences · doi:10.1137/080716542 · Cited by 11,952 · cited 1×
“Note that this property can be also employed in other optimization methods ( e.g ., iterative shrink-age/thresholding algorithms ISTA [4, 14] and FISTA [3]) as long as there involves a denoising subproblem.”
Flagged first, then by citations
6 of the 81 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- foi, 2006, Pointwise shape adaptive DCT denoising with structure preservation in luminance-chrominance space, International Workshop on Video Processing and Quality Metrics for Consumer Electronics
- danielyan, 2010, Image deblurring by augmented lagrangian with BM3D frame prior, Workshop on Information Theoretic Methods in Science and Engineering, 16
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M-Denoiser: Unsupervised image denoising for real-world optical and electron microscopy data
Chong, X., Cheng, M., Fan, W., Li, Q., & Leung, H. W. H. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107308 · Cited by 10
Denoiser-guided image deconvolution with arbitrary boundaries and incomplete observations
He, L., Gao, S., Deng, L.-J., Wang, Y., & Wang, C. (2023) · Signal Processing · doi:10.1016/j.sigpro.2023.109226 · Cited by 10
Double Transformer Super-Resolution for Breast Cancer ADC Images
Yang, Y., Xiang, T., Lv, X., Li, L.-H., Lok, M.-L., & Zeng, T.-Y. (2023) · IEEE journal of biomedical and health informatics · doi:10.1109/jbhi.2023.3341250 · Cited by 11
“Existing DL-based SR methods is mainly based on convolutional neural network (CNN) via the specific designation of network architectures, such as very deep network [22], [23], residual network [24], [25], [26], dense network [27], and generative adversarial network [28].”
MethodZhong, S., Zhao, X., Liu, D., Su, H., Xie, Z., & Fan, B. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3394154 · Cited by 10
“The PnP-IR strategy initially utilizes a specific variational segmentation algorithm to separate the data term and the prior term of the target objective function, thereby implementing an alternating iterative solution process between the data subproblem and the prior subproblem [27], [39].”
BackgroundMethodLightweight interactive feature inference network for single-image super-resolution
Li, W., Li, X., Wei, T., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-62633-8 · Cited by 10
hBP-Fi: Contactless Blood Pressure Monitoring via Deep-Analyzed Hemodynamics
Cao, Y., Zhang, S., Li, F., Chen, Z., & Luo, J. (2024) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom52122.2024.10621267 · Cited by 12
“The combination of mathematical model and DL have proven effective in various tasks [37], often achieved through a cascading approach: using a rough mathematical model for initialization and refining the results with DL, or using DL to extract features as prior information for an accurate…”
Zhao, X., Tian, H., Li, Z., et al. (2024) · Measurement · doi:10.1016/j.measurement.2024.115834 · Cited by 10
Integrating Kalman filter noise residue into U-Net for robust image denoising: the KU-Net model
Soniya, S., & Sriharipriya, K. C. (2024) · Scientific Reports · doi:10.1038/s41598-024-74777-8 · Cited by 12
Ma, C., Mi, J., Gao, W., et al. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129121 · Cited by 10
Fernández, S., Martínez, E., Varela, J., et al. (2024) · doi:10.1145/3697090.3697094 · Cited by 10
“5, this is only a third of the pixel rate at a resolution of 1920 × 1080@60Hz (resulting in 1 / 𝑇 = 148 MHz), meaning that each sample 𝑦 [ 𝑙 ] will actually be a linear combination of several tens of encoded bits, further complicating the image reconstruction.”
An Overview Study of Deep Learning in Geophysics: Cross-Cutting Research to Advance Geoscience
Wenxue, Z., Shikun, D., Hongjun, T., Dexiang, Z., Ying, Z., & Jiang, F. (2025) · IEEE Access · doi:10.1109/access.2025.3586693 · Cited by 12
Characterizing Sources of Ineffectual Computations in Deep Learning Networks
Nikolić, M., Mahmoud, M., Moshovos, A., et al. (2019) · IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) · doi:10.1109/ispass.2019.00030 · Cited by 10
“IRCNN 25 [39] Image De-noising CBSD68 [35] +Set5 [37]+Set14 [38] 108 50 Computational Imaging”
Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks
Helou, M. E., Zhou, R., & Susstrunk, S. E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58517-4_44 · Cited by 43
“To address blind SR, which is degradation-agnostic, recent methods propose to incorporate the degradation parameters including the blur kernel into the network [46,57,59,60].”
BackgroundMethodDemosaicing by Differentiable Deep Restoration
Tang, J., Li, J., & Tan, P. (2021) · Applied Sciences · doi:10.3390/app11041649 · Cited by 10
Malik, J., Serkan Kiranyaz, B., Yamaç, M., & Gabbouj, M. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506240 · Cited by 14
“Similarly, studies in [16] and [17] proposed different modifications to improve denoising such as batch renormalization, skip connections, and dilated convolutions.”
BackgroundBilateral Upsampling Network for Single Image Super-Resolution With Arbitrary Scaling Factors
Zhang, M., & Ling, Q. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3071708 · Cited by 10
“To flexibly handle various scaling factors and degradation models in reality, model-based plug-and-play methods [18], [19], [20], [21] try to plug the learned CNN prior into the iterative solution under the MAP(maximum a posterior) framework.”
MethodLow-Frequency Seismic Noise Reduction Based on Deep Complex Reaction–Diffusion Model
Zhang, Y., Lin, H., Li, Y., et al. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3086317 · Cited by 10
“[40] integrated the CNN model into the model-based optimization method to handle different inverse problems by making the CNN model as a denoiser prior, which not only retained the flexibility of traditional model-based optimization method but also improved the overall performance based on the powerful learning abilit…”
BackgroundMethodInfluentialJoint Anomaly Detection and Inpainting for Microscopy Images Via Deep Self-Supervised Learning
Huang, L., Cheng, D., Yang, X., et al. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506454 · Cited by 10
“For inpainting experiments, we compare to state-of-the-art methods such as IDBP [26], IRCNN [6], and TSLRA [5].”
BackgroundMethodInfluentialPhase retrieval with physics informed zero-shot network
Kumar, S. (2021) · Optics Letters · doi:10.1364/ol.433625 · Cited by 10
“In learning based object-prior block in figure 1, a deep CNN based on the U-Net and the ResNet architecture, referred as the DRUNet [27, 28] has been used.”
MethodUnrolled Optimization With Deep Learning-Based Priors for Phaseless Inverse Scattering Problems
Deshmukh, S., Dubey, A., & Murch, R. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3214495 · Cited by 10
Densely Connected Dilated Residual Network for Image Denoising: DDR-Net
Acar, V., & Ekşioğlu, E. M. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-11100-4 · Cited by 10
“Thirdly, the proposed denoiser gives better performance than state-of-the-art image denoising methods and networks such as BM3D [1], DnCNN [4], IRCNN [5], FFDNet [6] and MemNet [8] according to experimental results.”
MethodResultInfluentialCross-Scale KNN Image Transformer for Image Restoration
Lee, H., Choi, H., Sohn, K., & Min, D. (2023) · IEEE Access · doi:10.1109/access.2023.3242556 · Cited by 10
“By introducing residual learning that models degradation as residuals image, [7], [33], [34] surpass the methods directly estimate clean image [35], [36].”
MethodOut-of-Focus Image Deblurring for Mobile Display Vision Inspection
Min, S.-J., Kong, K., & Kang, S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3241931 · Cited by 10
“most non-blind deblurring schemes [27], [28].”
Li, W., Guan, Y., Huang, D., et al. (2023) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2023.107144 · Cited by 10
He, W., Li, G., Zhong, C., & Wang, Y. (2023) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-022-03475-8 · Cited by 10
“For example, the deep feed-forward network and convolutional neural network can be used to represent the data structure and become popular in many fields, such as image processing (Zhang et al. 2017), UQ (Zhu et al. 2019) and system identification (Zhou et al. 2021).”
MethodDual-branch deep image prior for image denoising
Xu, S., Cheng, X., Luo, J., Xiao, N., Xiong, M., & Zhou, C. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.103821 · Cited by 10
Relation-consistency graph convolutional network for image super-resolution
Yang, Y., Qi, Y., & Qi, S. (2023) · The Visual Computer · doi:10.1007/s00371-023-02805-1 · Cited by 10
SLN-RED: Regularization by Simultaneous Local and Nonlocal Denoising for Image Restoration
He, L., Zhang, Q., Yang, X., Wang, Y., & Wang, C. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3265174 · Cited by 10
Gao, Y., Wei, M., Zhu, J., Wang, Y., Zhang, Y., & Lin, T. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3265757 · Cited by 10
“Thus, the Gaussian noise with five SNR levels (the SNRs of the noisy signals are − 15, − 10, − 5, 0, and 5 dB, respectively) has been added to the theoretical signals, so as to construct the noisy dataset [42].”
BackgroundA Unified Acceleration Solution Based on Deformable Network for Image Pixel Processing
Mao, W., Su, Z., Luo, J., et al. (2023) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2023.3272050 · Cited by 10
PyNET-Q×Q: An Efficient PyNET Variant for Q×Q Bayer Pattern Demosaicing in CMOS Image Sensors
Cho, M., Lee, H., Je, H., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3272665 · Cited by 10
“CNN-based [9], [20], [34], [35], [36], [37], [38] and GANbased [39], [40] super-resolution models generate photorealistic output images, surpassing classical methods without using hand-crafted elements.”
Panigrahi, S. K., Tripathy, S. K., Bhowmick, A., Satapathy, S. K., Barsocchi, P., & Bhoi, A. K. (2024) · IEEE Access · doi:10.1109/access.2024.3364397 · Cited by 10
“…techniques describing the essential qualitative and quantitative features of the image, the existing methods can be broadly categories into the model based [4], [5], [12], [13], [14], [15] and the discriminative learning based methods [8], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25].”
MethodSpherical Pseudo-Cylindrical Representation for Omnidirectional Image Super-resolution
Cai, Q., Li, M., Ren, D., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27846 · Cited by 10
UniProcessor: A Text-Induced Unified Low-Level Image Processor
Duan, H., Min, X., Wu, S., Shen, W., & Zhai, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72855-6_11 · Cited by 15
DEEP NEURAL NETWORKS FOR NON-LINEAR MODEL-BASED ULTRASOUND RECONSTRUCTION
Almansouri, H., Venkatakrishnan, S. V., Buzzard, G. T., et al. (2018) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2018.8646704 · Cited by 10
“Alternatively, researchers have adapted variable splitting strategies such as the Plug-and-Play approach [16], [17] to iteratively solve two learned sub-problems corresponding to a forward-model inversion and a denoising step in order to determine a fixed point [13], [18]–[22].”
MethodRemote Sensing Image Colorization Based on Multiscale SEnet GAN
Wu, M., Jin, X., Jiang, Q., et al. (2019) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei48845.2019.8965902 · Cited by 10
“As a research of image processing, it has been applied in many fields, such as film and television animation [1], old photo restoration [2], and medical images processing [3].”
MethodRahim, T., Khan, S., Usman, M. A., et al. (2020) · IETE Technical Review · doi:10.1080/02564602.2020.1721342 · Cited by 10
Self-Convolution: A Highly-Efficient Operator for Non-Local Image Restoration
Guo, L., Zha, Z., Ravishankar, S., & Wen, B. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414124 · Cited by 9
A multiscale dilated residual network for image denoising
Li, D., Chen, H., Jin, G., Jin, Y., Zhu, C., & Chen, E. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09113-z · Cited by 10
“Second, compared with discriminative learning based methods (MLP [2], TNRD [5], and IRCNN [36]), the proposed method outperforms all of them on both BSD68 and Set12 dataset, and the PSNR gain is favorable (e.g., σ = 50, the PSNR gain is more than 0.13dB on the BSD68 dataset).”
BackgroundMethodInfluentialPartial Convolutional LSTM for Spatiotemporal Prediction of Incomplete Data
Son, H., & Jang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3022774 · Cited by 10
A multi-scale generative adversarial network for real-world image denoising
Yu, X., Fu, Z., & Ge, C. (2021) · Signal Image and Video Processing · doi:10.1007/s11760-021-01984-5 · Cited by 10
“The denoising convolutional neural network (DnCNN) [17], fast and flexible denoising convolutional neural network (FFDNet) [18] and image restoration convolutional neural network (IRCNN) [19] even outperform the startof-the-art model-based ones.”
BackgroundDual-Complementary Convolution Network for Remote-Sensing Image Denoising
Jia, X., Peng, Y., Li, J., Ge, B., Xin, Y., & Liu, S. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3101851 · Cited by 10
Plug-and-Play ADMM for MRI Reconstruction With Convex Nonconvex Sparse Regularization
Li, J., Li, J., Xie, Z., & Zou, J. X. (2021) · IEEE Access · doi:10.1109/access.2021.3124600 · Cited by 10
Semi‐supervised image super‐resolution with attention CycleGAN
Hou, M., He, X., Dou, F., et al. (2021) · IET Image Processing · doi:10.1049/ipr2.12401 · Cited by 10
A New Generative Adversarial Network for Texture Preserving Image Denoising
Qu, Z., YuanQi, Z., Sun, Y., et al. (2018) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2018.8608126 · Cited by 10
“CNN with deep architecture [11,12,13] is widely applied, relying on its feature extraction and generalization characterizations.”
MethodLearning Deep Image Priors for Blind Image Denoising
Hou, X., Luo, H., Liu, J., Xu, B., Sun, K., Gong, Y., Liu, B., & Qiu, G. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00224 · Cited by 10
“We compare our method with state of the art method DnCNN-B on different datasets.”
BackgroundInfluentialLearned Image Deblurring by Unfolding a Proximal Interior Point Algorithm
Corbineau, M.-C., Bertocchi, C., Chouzenoux, É., Prato, M., & Pesquet, J.-C. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803438 · Cited by 9
“The architecture of the post-processing layer Lpp, which is depicted in Figure 3, is inspired from [4]: it is made of 9 convolutional layers with different dilation factors, so as to widen the receptive field without creating memory issues.”
BackgroundMethodInfluentialLD-Net: An Efficient Lightweight Denoising Model Based on Convolutional Neural Network
Le, T.-H., Lin, P.-H., & Huang, S.-C. (2020) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2020.3012757 · Cited by 10
Deep Iterative Residual Convolutional Network for Single Image Super-Resolution
Umer, R. M., Foresti, G. L., & Micheloni, C. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412159 · Cited by 10
“Model-based optimization [5] methods with the integration of deep CNNs priors can improve efficiency, but due to hand-crafted parameters, they are not suitable for end-to-end deep learning methods.”
BackgroundImage Restoration by Deep Projected GSURE
Abu-Hussein, S., Tirer, T., Chun, S. Y., Eldar, Y. C., & Giryes, R. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00017 · Cited by 10
“Note that several P&P methods have also shown excellent results using offline trained CNN denoisers [25, 43, 50, 43].”
BackgroundMethodSparse Weight Activation TrainingPreprint
Raihan, A., & Aamodt, T. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.01969 · Cited by 40
“Convolutional Neural Networks (CNNs) are effective at many complex computer vision tasks including object recognition [30, 60], object detection [59, 53] and image restoration [14, 68].”
BackgroundBayesian Imaging Using Plug & Play Priors: When Langevin Meets Tweedie
Laumont, R., Bortoli, V. D., Almansa, A., Delon, J., Durmus, A., & Pereyra, M. (2022) · SIAM Journal on Imaging Sciences · doi:10.1137/21m1406349 · Cited by 123
Image-Adaptive GAN Based Reconstruction
Abu-Hussein, S., Tirer, T., & Giryes, R. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5708 · Cited by 9
“For example, a method which is not based on a generative model cannot produce a perceptually pleasing image of human face if the eyes are completely missing in an inpainting task (Yeh et al. 2017).”
Variational Model-Based Deep Neural Networks for Image Reconstruction
Chen, Y., Ye, X., & Zhang, Q. (2021) · Springer eBooks · doi:10.1007/978-3-030-03009-4_57-1 · Cited by 10
MFAENet: A Multiscale Feature Adaptive Enhancement Network for SAR Image Despeckling
Liu, S., Zhang, L., Tian, S., et al. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3327332 · Cited by 9
Self-citationNGDCNet: Noise Gating Dynamic Convolutional Network for Image Denoising
Zhu, M., & Li, Z. (2023) · Electronics · doi:10.3390/electronics12245019 · Cited by 9
“To assess the denoising performance of NGDCNet, we conducted a visual comparison with several competing methods, including FFDNet [39], DnCNN [19], DRUNet [56], IRCNN [18], and NIFBGDNet [57].”
BackgroundMethodInfluentialAn Efficient Multiscale Spatial Rearrangement MLP Architecture for Image Restoration
Hua, X., Li, Z., & Hong, H. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3341700 · Cited by 9
Deep, Convergent, Unrolled Half-Quadratic Splitting for Image Deconvolution
Zhao, Y., Li, Y., Zhang, H., Monga, V., & Eldar, Y. C. (2024) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2024.3377132 · Cited by 10
“Zhang et al. [25] train a set of fast and effective CNN denoisers and integrate them into a model-based optimization method of HQS to solve the image deblurring problem.”
BackgroundMethodVariational Bayes Image Restoration With Compressive Autoencoders
Biquard, M., Chabert, M., Genin, F., Latry, C., & Oberlin, T. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3564829 · Cited by 11
“Their key concept is to use Gaussian denoisers [12], and in particular deep denoisers [13] in place of the proximal operator of the regularization.”
A Review of Unmanned Visual Target Detection in Adverse Weather
Song, Y., & Lu, Y. (2025) · Electronics · doi:10.3390/electronics14132582 · Cited by 13
Multi-scale Dense Network for Single-image Super-resolution
Chang, C.-Y., & Chien, S. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683719 · Cited by 9
“The dilation convolution is also adapted to handle image denoise problem in the IRCNN [23] proposed by Zhang et al. and DDRN [24] proposed by Wang et al. .”
MethodMulti-Resolution Space-Attended Residual Dense Network for Single Image Super-Resolution
Qin, J., Sun, X., Yan, Y., Jin, L., & Peng, X. (2020) · IEEE Access · doi:10.1109/access.2020.2976478 · Cited by 9
Median Filter Aided CNN Model for Removal of Gaussian Noise from Images
Dey, S., Bhattacharya, R., & Sarkar, R. (2020) · IEEE Recent Advances in Intelligent Computational Systems · doi:10.1109/raics51191.2020.9332492 · Cited by 9
“25 ADNet With Dilation (2) 29.70 32.51 30.54 29.08 29.90 28.94 28.64 31.95 29.32 29.94 29.85 29.76 IRCNN 29.87 32.52 30.60 29.02 29.89 28.90 29.26 32.00 29.10 29.89 29.83 29.72 ADNet With Dilation (8).”
MethodInfluentialHe, W., Li, G., & Nie, Z. (2022) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-021-03120-w · Cited by 9
“…MCS. (45) W = M t − max ( M ( 0, 0.3 )) , To simulate the stochastic loads described by Gaussian processes, the expansion optimal linear estimation (Zhang et al. 2017a, b) is used to approximate the loads, F i where U k , k = 1, …, d , are the independent standard normal variables, Ξ is the…”
MethodInfluentialImage restoration via exponential scale mixture‐based simultaneous sparse prior
Yuan, W., Liu, H., & Liang, L. (2022) · IET Image Processing · doi:10.1049/ipr2.12563 · Cited by 9
MLFAN: Multilevel Feature Attention Network With Texture Prior for Image Denoising
Ulu, A., Yıldız, G., & Dızdaroğlu, B. (2023) · IEEE Access · doi:10.1109/access.2023.3264604 · Cited by 9
“In [20], Zhang et al. proposed a new network (IRCNN) for image denoising combining model-based and learning-based methods via using half quadratic splitting (HQS) and CNN.”
MethodSaturation-Value Blind Color Image Deblurring with Geometric Spatial-Feature Prior
Zhang, H., Fang, Y., Wong, H. S., Li, L., & Zeng, T. (2023) · Communications in Computational Physics · doi:10.4208/cicp.oa-2022-0226 · Cited by 9
Local to non-local: Multi-scale progressive attention network for image restoration
Shen, L., Zhao, B., Li, Q., Zhang, C., Sun, X., & Peng, B. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103725 · Cited by 9
Lightweight image denoising network with four-channel interaction transform
Wang, J., Lu, Y., & Lu, G. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104766 · Cited by 9
Li, M., Zhang, Z., Chen, S., Xu, Z., Li, Q., Feng, H., & Chen, Y. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3300549 · Cited by 9
“Image restoration has been a fundamental and rapidly developing problem in image processing [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32].”
BackgroundTwo-branch crisscross network for realistic and accurate image super-resolution
Yang, Q., Yang, Q., Liu, Y., et al. (2023) · Displays · doi:10.1016/j.displa.2023.102549 · Cited by 9
Deep learning in optics—a tutorial
Hadad, B., Froim, S., Yosef, E., Giryes, R., & Bahabad, A. (2023) · Journal of Optics · doi:10.1088/2040-8986/ad08dc · Cited by 10
Dual Frequency Transformer for Efficient SDR-to-HDR Translation
Xu, G., Hou, Q., & Cheng, M. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1418-8 · Cited by 9
He, W., Li, G., Zeng, Y., et al. (2024) · Structural Safety · doi:10.1016/j.strusafe.2024.102450 · Cited by 9
Convergent Plug-and-Play with Proximal Denoiser and Unconstrained Regularization Parameter
Hurault, S., Chambolle, A., Leclaire, A., et al. (2024) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-024-01195-w · Cited by 9
“Moreover, when α = 1,algorithms α PGD(51)andPGD(26)areequivalent,butwe get a slightly worst bound in Theorem 2 than in Theorem 1 (cid:19) (cid:20) ).”
Boulch, A., Trouvé, P., Colin‐Koeniguer, E., et al. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8519370 · Cited by 9
“For the experiments we use the network from [9] (referred in the following as DCNN for Dilated Convolutional Neural Network) trained in a residual fashion.”
BackgroundMethodLearning Optimal Phase-Coded Aperture for Depth of Field Extension
Akpinar, U., Şahin, E., & Gotchev, A. P. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803419 · Cited by 9
“The residual learning have been proven to be useful for image deblurring in the literature [11, 12, 13, 8].”
BackgroundMethodWang, X., Liu, F., & Ma, X. (2021) · Signal Image and Video Processing · doi:10.1007/s11760-020-01824-y · Cited by 9
“[15] to handle multiple scales super-resolution, image deblocking and image denoising.”
BackgroundSeo, J., Yang, Y., Hong, Y., et al. (2021) · IET Radar Sonar & Navigation · doi:10.1049/rsn2.12105 · Cited by 9
“We note that deep neural networks have been successfully applied to image restoration/classification tasks because of their outstanding performances in adaptability and feature extraction [17, 18].”
MethodMulti-wavelet guided deep mean-shift prior for image restoration
Zhang, M., Yang, C., Yuan, Y., et al. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116449 · Cited by 9
Interactive Multi-Dimension Modulation for Image Restoration
He, J., Dong, C., Liu, Y., & Qiao, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3129345 · Cited by 8
“To further demonstrate the effectiveness of our proposed CResMD, we consider the comparison with five state-of-the-art denoising methods (CBM3D [32], DnCNN [12], IRCNN [33], FFDNet [16], and MIRNet [34]) on CBSD68 dataset for noise levels 15, 25, and 50.”
MethodA Novel Un-Supervised GAN for Fundus Image Enhancement with Classification Prior Loss
Chen, S., Zhou, Q., & Zou, H. (2022) · Electronics · doi:10.3390/electronics11071000 · Cited by 8
Simultaneous Nonlocal Low-Rank And Deep Priors For Poisson Denoising
Zha, Z., Wen, B., Yuan, X., Zhou, J., & Zhu, C. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746870 · Cited by 10
Searching Efficient Model-Guided Deep Network for Image Denoising
Ning, Q., Dong, W., Li, X., & Wu, J. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3231741 · Cited by 9
“By unfolding the iterative updating algorithm through a deep network, model-guided methods [27], [36], [38] have achieved excellent performance.”
BackgroundMethodProgressive Feature Fusion Attention Dense Network for Speckle Noise Removal in OCT Images
Zeng, L., Huang, M., Li, Y., Chen, Q., & Dai, H. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3205217 · Cited by 9
“We compare PFFADN with BM3D [37], TNRD [38], ECND-Net [39], IRCNN [40], FFDNet [41], DnCNN [42], and Con-denseNet [31] to evaluate the denoising effect of the proposed method.”
MethodDeep CNN denoiser prior for blurred images restoration with multiplicative noise
Li, Y., Hu, S., Ni, G., et al. (2023) · Inverse Problems and Imaging · doi:10.3934/ipi.2022075 · Cited by 9
Arya, A. S., Saha, A., & Mukhopadhyay, S. (2023) · The Visual Computer · doi:10.1007/s00371-023-02786-1 · Cited by 9
A Convergent Neural Network for Non-Blind Image Deblurring
Zhao, Y., Li, Y., Zhang, H., Monga, V., & Eldar, Y. C. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222656 · Cited by 9
“We compare our results with state-of-the-art algorithms, including EPLL [11], BM3D [10], and SVMAP [17] (iterative algorithms with some learned components), and MLP [14], IRCNN [25], FCNN [26], and FNBD [27] (fully deep learning methods).”
ResultLyu, Q., Ruan, D., Hoffman, J. M., Neph, R., McNitt‐Gray, M. F., & Sheng, K. (2019) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2512484 · Cited by 9
Image Deblurring Utilizing Inertial Sensors and a Short-Long-Short Exposure Strategy
Yang, C., Feng, H., Xu, Z., et al. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2973499 · Cited by 9
“In addition, with the development of the deep convolutional neural network (CNN) technique, several approaches [22]–[24] have attempted to solve the image restoration problem in a learning framework.”
BackgroundUnsupervised detail-preserving network for high quality monocular depth estimation
Zhang, M., Ye, X., & Fan, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.015 · Cited by 9
An effective method of contour extraction for SEM image based on DCNN
Zhou, T., Shi, X., Yanyan, et al. (2020) · International Workshop on Advanced Patterning Solutions (IWAPS) · doi:10.1109/iwaps51164.2020.9286798 · Cited by 9
“Usually, the residual connections could make a better performance on convergence, however, the final effect is little difference compared with non-residual connections for some deep plain networks [8].”
BackgroundA Generative Adversarial Network-Based Image Denoiser Controlling Heterogeneous Losses
Cho, S. I., Park, J. H., & Kang, S. (2021) · Sensors · doi:10.3390/s21041191 · Cited by 10
“These conventional denoising methods can be categorized as model-based optimization methods and deep learning-based methods [1].”
BackgroundMethodInfluentialDesigning and Training of A Dual CNN for Image DenoisingPreprint
Tian, C., Xu, Y. D., Zuo, W., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03951 · Cited by 9
“86 IRCNN [16] 32.”
BackgroundMethodSelf-citationSun, Y., Liu, J., Sun, Y., Wohlberg, B. E., & Kamilov, U. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01446 · Cited by 9
“Similar priors are extensively used in various PnP and RED algorithms [14, 19, 30].”
MethodProgressive Training of Multi-level Wavelet Residual Networks for Image DenoisingPreprint
Peng, Y., Cao, Y., Liu, S., Yang, J., & Zuo, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12422 · Cited by 15
“Ground-truth Noisy image BM3D [4] TNRD [18] DnCNN [5] IRCNN [19] RED [6] MemNet [20] N3Net [21] NLRN [22] MWCNN [7] PT-MWRN Fig.”
InfluentialSelf-citationKim, J., Jeong, K., & Kang, M. G. (2022) · Sensors · doi:10.3390/s22114285 · Cited by 8
Khan, B., Mumtaz, A., Zafar, Z., Sedky, M., Benkhelifa, E., & Fraz, M. M. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01477-0 · Cited by 9
Texture-guided CNN for image denoising
Zhang, Q., Xiao, J., Zhang, S., Lin, J. C., Tian, C., & Zhang, C. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17670-2 · Cited by 9
“CNNs have been quite effective in images with additive white noise [19], real-world noise [20], and blind denoising [21].”
Wang, Y., Yuan, W. Q., Xie, F., & Lin, B. (2024) · Remote Sensing · doi:10.3390/rs16111956 · Cited by 10
Metadata
- Authors
- Zhang, K., Zuo, W., Gu, S., & Zhang, L.
- Affiliations
- Hong Kong Polytechnic University (HK)
- Harbin Institute of Technology (CN)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 2808-2817
- OpenAlex
- W2613155248
- Cited by
- 2,116
- References
- 81
- Citations by year
- 2026: 80 · 2025: 191 · 2024: 248 · 2023: 293 · 2022: 272 · 2021: 338 · 2020: 297 · 2019: 240 · 2018: 135 · 2017: 10
- FWCI
- 49.67
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
- TUScholarShare (Temple University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- image restoration, model-based optimization, discriminative learning, variable splitting, inverse problems, low-level vision, Gaussian denoising, image deblurring
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