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.”
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A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
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“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.
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Li, M., Liu, W., & Chen, W. (2024) · IEEE International Conference on Advanced Infocomm Technology · doi:10.1109/icait62580.2024.10807930 · Cited by 1
Tao, J., Geng, T., Han, C., Li, S., Song, C., & Wang, X. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3539776 · Cited by 1
“Both the IRCNN method [66] and the PnP-ADMM (abbreviated as PA in the names) method are universal image reconstruction frameworks, including different OM application modules such as denoising, deblurring, and SR.”
Gao, R., Jin, H., Wang, T., Li, X., & Liu, Q. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.2.023002 · Cited by 2
Self-supervised blind image super-resolution via alternately optimization
Li, Y., Yu, J. X., & Xiao, C. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-03955-6 · Cited by 1
“(2),weuseanalternate optimization method to solve the following image and blur kernel estimation sub-problems: Zhang et al. [20] train a deep denoising network on external datasets to solve the image restoration problem by combining it with the regularization model.”
Chen, J., Yue, J., Zhou, H., & Hu, Z. (2025) · Sensors · doi:10.3390/s25092672 · Cited by 1
“The proposed algorithm was evaluated against BM3D [3], WNNM [5], IRCNN [58], DnCNN [13], FFDNet [14], ADNet [15], MAFNet [54], and DRUNet [59] using railway freight car testing datasets.”
Liao, C.-L., Ding, J., & Lu, D.-Y. (2025) · IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) · doi:10.3390/engproc2025092047 · Cited by 1
EIMF: An Enhanced Iterative Mean Filter for Effective Denoising of Medical CT Images
Singha, B. J., Jitani, N., Sarmah, R., et al. (2025) · SN Computer Science · doi:10.1007/s42979-025-03989-0 · Cited by 1
“In the deep learning domain, Zhang et al. developed several CNN-based models: DnCnn [18] for blind Gaussian denoising using residual learning, IrCnn [19] for integrating CNN priors into iterative solvers, and FfdNet [20], which supports variable noise levels through input noise maps.”
Rapid eigenpatch utility classifier for image denoising
Mitchell, M. A. J., Sanvito, S., & Jones, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-96859-x · Cited by 1
“Though acknowledging the utility of image priors with respect to computational speed 30 , in the experimental context it is preferable to edit the data as little as possible, and so the use of a preparatory data smoothing, through techniques like total variation regularisation or imposing a…”
Arya, A. S., & Mukhopadhyay, S. (2025) · Signal Processing Image Communication · doi:10.1016/j.image.2025.117341 · Cited by 1
Linear Convergence of Plug-and-Play Algorithms With Kernel Denoisers
Sinha, A., Kumar, B., Athalye, C. D., & Chaudhury, K. N. (2025) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2025.3573044 · Cited by 2
“PnP gives state-of-the-art results with trained denoisers [6], [8], [13], [33].”
Self2Channel: Self-supervised denoising of different regions using coalition game based channel mask
Song, B., Si, Y., & Li, K. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104518 · Cited by 1
Adaptive multi-modal prompting for universal image restoration amidst diverse degradations
Zhang, S., Wang, J., He, N., et al. (2025) · The Visual Computer · doi:10.1007/s00371-025-04023-3 · Cited by 1
A computationally efficient and lightweight hybrid deep residual network for fundus image denoising
Kumar, B., & Kumar, B. (2025) · Optics & Laser Technology · doi:10.1016/j.optlastec.2025.113656 · Cited by 1
SSH-Net: A self-supervised and hybrid network for noisy image watermark removal
Liu, W., Gao, J., & Yap, K. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104516 · Cited by 1
Optimizing Regularization Parameters for Potential Field Data Inversion Using RPSNN
Jiang, Z., Ma, G., Wang, T., et al. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3590377 · Cited by 1
“Initially, these machine learning techniques were used for post-processing tasks, for instance, to improve solution quality or perform tasks such as image classification [18].”
Lu, 张. Z., Pengju, 刘. L., & Shigang, 刘. L. (2025) · Laser & Optoelectronics Progress · doi:10.3788/lop242328 · Cited by 1
ARF: Arbitrary Routing Framework for All-in-One Image Restoration
Xu, Y., Gao, N., Zhong, Y., Chao, F., & Ji, R. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3604401 · Cited by 1
“1], DnCNN [18], IRCNN [41], FFDNet [42], AirNet [22], and ARF, respectively, and (h) is the ground truth of clean images.”
Wang, F., Zhang, X.-Z., & Song, X. (2025) · Seismological Research Letters · doi:10.1785/0220240437 · Cited by 1
DSRIR: Dynamic spatial refinement learning for progressive all-in-one image restoration
Wang, X., Liu, X., Yang, Y., Wang, Z., He, X., Chen, H., Li, Y., & Wang, P. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104380 · Cited by 1
prNet: Efficient and Robust Phase Retrieval via Stochastic Refinement
Kaya, M. O., & Öktem, F. S. (2025) · Machine learning for signal processing ... · doi:10.1109/mlsp62443.2025.11204288 · Cited by 1
“The training set consists of 44 , 000 natural images, including the dataset used in [11, 13] as well as randomly selected images from ImageNet [28, 29].”
Single-Layer Denoising Taylorformer for UAV Nighttime Tracking
Su, Z., Wang, H., & Qi, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3398-5_11 · Cited by 1
Zhang, Y. (2025) · Journal of Imaging · doi:10.3390/jimaging11110405 · Cited by 1
“Architectures [19–24] represent a step towards generality, handling multiple degradations within a single network framework.”
Deep Priors for Satellite Image Restoration With Accurate Uncertainties
Biquard, M., Chabert, M., Genin, F., Latry, C., & Oberlin, T. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3633774 · Cited by 2
“Their key concept is to use Gaussian denoisers [39], in particular, deep denoisers [40] in place of the proximal operator of the regularization.”
PLPGR-Net: Photon-Level Physically Guided Restoration Network for Underwater Laser Range-Gated Image
Tian, Q., Hu, L., Zhang, Z., et al. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13122217 · Cited by 1
Long, Y., Yan, S., Fu, R., Zheng, J., Li, J., & Chen, H. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3633700 · Cited by 1
“IRCNN [30], recognized for its computational ef-ficiency, flexibility, and excellent detail preservation, has shown outstanding performance in seismic data processing, making it highly suitable as a prior denoiser within the inverse framework.”
Mean-Curvature-Regularized Deep Image Prior with Soft Attention for Image Denoising and Deblurring
Israr, M., Ahmad, S., Asghar, M. N., & Arif, S. (2025) · Mathematics · doi:10.3390/math13243906 · Cited by 1
MSJA-Net: Multi-path joint attention network for feature-space compressive sensing
Xiang, J., Xie, X., & Wang, L. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04955-2 · Cited by 1
Hojjat, M., & Shayegan, M. J. (2025) · European journal of medical research · doi:10.1186/s40001-025-03579-z · Cited by 1
“IRCNN (Iterative Residual CNN) [43] is a recursive CNN that refines denoising iteratively but increases computational complexity with its iterative nature.”
Deep Convolutional Primal-Dual Network for Image Deblurring
Jiu, M., Peng, M., Zhang, F., Li, S., Zhao, H., Ji, R., & Xu, M. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3647770 · Cited by 1
“A great plenty of methods can be found in [1], for instance, WCNN [14], RCNN [15], and recent Transformers [16].”
Dynamic–static hybrid dictionary learning: enhancing deep K-SVD for image denoising and beyond
Sun, Z., Yao, J., & Zhang, C. (2026) · The Visual Computer · doi:10.1007/s00371-026-04361-w · Cited by 1
“Beyond the above K-SVD-based algorithms, this exper-iment also includes several traditional denoisers such as BM3D [50] and WNNM [51], as well as representative deep learning-based approaches, including DnCNN [45] and IRCNN [46].”
Yahaya, M. M., Kumam, P., & Sıtthıthakerngkıet, K. (2026) · IEEE Access · doi:10.1109/access.2026.3661859 · Cited by 1
“These networks perform incredibly well when trained for a specific restoration task and under a given distortion scenario [42], [43], [44], [45].”
Akhilaraj, D., & Zacharias, J. (2026) · Circuits Systems and Signal Processing · doi:10.1007/s00034-026-03497-3 · Cited by 1
“BM3D[8], DnCNN-S[63], TNRD[7], BUIFD[57], IRCNN[65], FFDNet[66], DSNetB[30], ADNet[31], AINDNet[42], NIFBGDNet[29], NLED[3], S2S[39], R2R[33], CDNet[5], DudeNet[54], and MWDCNN[53].”
Zhang, J., Ren, G., Wang, X., et al. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00653-2 · Cited by 1
“The involved methods include: BM3D (Kang et al. 2013), DnCNN (Zhang et al. 2017a), IRCNN (Wang et al. 2023), RED-CNN (Liang et al. 2020), Uformer (Wang et al. 2022), CTformer (Wang et al. 2023), EP-GAMNet (Kang et al. 2024), HEDCNN (Jia et al. 2024), GE-MDCNet (Kang et al. 2023), and FSformer (Kang…”
Sparse Representation via Unfolding ℓ 0 -Regularized Dictionary Learning for Image Restoration
Zhao, Y., Zheng, Z., Dai, W., et al. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3679293 · Cited by 1
“Furthermore, Table V shows that (cid:96) 0 -GLDL-DUN is comparable to state-of-the-art methods [46], [59], [60], [69] on the BSD68 dataset.”
Frequency-Domain Feature Learning Network for Joint Image Demosaicing and Denoising
Zhang, D., Li, F., Yang, J., et al. (2026) · Mathematics · doi:10.3390/math14071175 · Cited by 1
Cai, E., Zhang, Y., Luo, M., Guo, J., & Guo, T. (2026) · Engineering Structures · doi:10.1016/j.engstruct.2026.122892 · Cited by 1
Chung, K., Tsai, S.-B., Tseng, Y.-L., et al. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2859579 · Cited by 1
“[42] proposed a successful super-resolution CNN method for multiple degradations (SRMD), in which the blur and noise degradations [41] are considered.”
MethodHigh-Dimensional Embedding Denoising Autoencoding Prior for Color Image Restoration
Yuan, Y., Zhou, J., He, Z., et al. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8804287 · Cited by 1
“Scale Dataset Bicubic SRCNN TNRD DnCNN-3 IRCNN DMSP SRMD DAEP MDAEP M2DAEP 2 Set 5 31.80 34.50 34.62 35.20 35.07 35.16 35.28 35.23 35.41 35.48 3 28.67 30.”
MethodInfluentialBlind Motion Deblurring for Online Defect Visual Inspection
Liu, G., Wang, B., & Wu, J.-F. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-0121-0_5 · Cited by 1
A Composite Discriminator for Generative Adversarial Network based Video Super-Resolution
Wang, X., Lucas, A. M., López-Tapia, S., et al. (2019) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2019.8903072 · Cited by 1
“Recent Super-Resolution (SR) works seem to indicate that learning-based methods produce more realistic images than model-based methods [1], [2].”
BackgroundStacked lossless deconvolutional network for remote sensing image restoration
Shin, C., Kim, M., Kim, S., & Kim, Y. (2020) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.14.016511 · Cited by 1
“17 The SLDN surpasses CBM3D 56 as well as the learning-based methods 10,12,35,44 with a large margin for all noise levels.”
BackgroundMethodInfluentialLqaid: Localized Quality Aware Image Denoising Using Deep Convolutional Neural Networks
Dendi, S. V. R., Dev, C., Kothari, N., et al. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053056 · Cited by 1
“8478 (f) IRCNN [8], 26.”
MethodInfluentialStick-Breaking Dependent Beta Processes with Variational Inference
Cao, Z., Zhao, J., & Sun, S. (2020) · Neural Processing Letters · doi:10.1007/s11063-020-10392-8 · Cited by 1
“Different to methods mentioned above, IRCNN [26] trains a set of fast and effective convolutional neural network (CNN) denoiser and can be directly used for image denoising.”
MethodDeep Neural Network-based Active Region Magnetogram Patch Super Resolution
Habeeb, M. S., Aydin, B., Ahmadzadeh, A., et al. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9377920 · Cited by 1
NASA: A Noise-Adaptive and Structure-Aware Learning Framework for Image Deblurring
Liu, X., Ma, L., Liu, R., et al. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414459 · Cited by 1
“Input Image IRCNN [9] FDN [14] GLRA [10] KerUnc [13] CPCR [12] Ours Then, the performance is slightly improved when only learning one auxiliary.”
BackgroundMethodU-OLANet : Low-Resolution One-Layer Attention Neural Networks for Image Denoising
Han, J. W., & Kang, D.-S. (2021) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2021.19.9.1 · Cited by 1
Joint Image Denoising and Colorization Using Deep Network
Khoa, T. V., Vinh, D. Q., Phuc, N. H., et al. (2020) · doi:10.1145/3426020.3426056 · Cited by 1
Learned Gradient Descent Performance in Bicubic Super-Resolution Task
Koshelev, I., & Somov, A. (2021) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon48115.2021.9589797 · Cited by 1
“Thus for the general image restoration problem, Zhang et al. [16] performed the half quadratic variable splitting and decoupled data fidelity term and regularization term into two individual subproblems.”
BackgroundHajjaj, M. (2021) · The Journal of Engineering · doi:10.1049/tje2.12120 · Cited by 1
MMPDNet: Multi-Stage & Multi-Attention Progressive Image Denoising
Xue, J., Liang, J., He, J., et al. (2021) · International Conference on Ubiquitous Computing and Communications (IUCC) · doi:10.1109/iucc-cit-dsci-smartcns55181.2021.00079 · Cited by 1
“In the early phase, a lot number based on the CNNs denoising models are proposed [4], [12], [13], in spite of them are gain great success by introducing CNNs into denoising tasks.”
BackgroundGenerative Adversarial Network of Industrial Positron Images on Memory Module
Zhu, M., Zhao, M., Yao, M., & Guo, R. (2022) · Entropy · doi:10.3390/e24060793 · Cited by 1
“[10] proposed a multi-scale CNN approach based on the joint optimization of image content and texture constraints to get higher quality images.”
BackgroundLandweber Iteration-Inspired Network for Image Super-Resolution
Li, X., Wang, L., & Liu, X. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/5337569 · Cited by 1
“IRCNN [20] analyzed the image restoration by half-quadratic splitting (HQS) strategy and designed an end-to-end network for multitask image restoration.”
A Dual-Branch Dilated Convolutional Neural Network for Image Denoising
Luo, M., Xi, P., Yan, Z., & Xin, Y. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-3927-3_31 · Cited by 1
Sparse-View CBCT Reconstruction Using Combined DRUNet and HQS
Jiang, L., Wang, X., Jiang, H., et al. (2022) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai55851.2022.9904278 · Cited by 1
“DRUNet and HQS Lan Jiang School of Information Engineering Southwest University of Science and Technology Mianyang, China 1379111436@qq.com Xueyuan Wang School of Information Engineering Southwest University of Science and Technology Mianyang, China 121053406@qq.com Xiaoyi Wang Mianyang Central Hospital School of Medi…”
MethodInfluentialSuper-Resolving Methodology for Noisy Unpaired Datasets
Min, S.-J., Jo, Y.-S., & Kang, S. (2022) · Sensors · doi:10.3390/s22208003 · Cited by 1
“Furthermore, several approaches have been considered to combine denoising with SR models [23].”
NBD-GAP: Non-Blind Image Deblurring without Clean Target Images
Nair, N. G., Yasarla, R., & Patel, V. M. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897543 · Cited by 1
“For comparison, we use a combination of classical non-blind deblurring algorithms [29], supervised deep networks designed for non-blind deblurring [9, 28, 17], methods which utilize a combination of supervised deep learning and optimization algorithms [27] and one supervised motion deblurring network [26].”
BackgroundMethodInfluentialA novel approach for image restoration using convolution network-based image denoising technique
Premnath, S. P., Renjit, J. A., Shanmugapriya, P., et al. (2022) · AIP conference proceedings · doi:10.1063/5.0108299 · Cited by 1
Zhu, Y., Gong, C., Liu, S., et al. (2023) · IET Image Processing · doi:10.1049/ipr2.12753 · Cited by 1
Variational Model-Based Deep Neural Networks for Image Reconstruction
Chen, Y., Ye, X., & Zhang, Q. (2023) · Springer eBooks · doi:10.1007/978-3-030-98661-2_57 · Cited by 1
Optical implementation and robustness validation for multi-scale masked autoencoder
Xue, Y., Su, X., Zhang, S., & Yuan, X. (2023) · APL Photonics · doi:10.1063/5.0139050 · Cited by 1
Deep Learning based Image Enhancing Environment with Noise Suppression
Sivanantham, Reddy, B. S. K., SaiGnaneswar, J., et al. (2023) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears56392.2023.10084977 · Cited by 1
An Efficient Self-Refinement and Reconstruction Network for Image Denoising
Xue, J., & Wu, Q. (2023) · International Journal of Information Technologies and Systems Approach · doi:10.4018/ijitsa.321456 · Cited by 1
A Bayesian Approach to Gaussian-Impulse Noise Removal Using Hessian Norm Regularization
Maji, S. K., & Saha, A. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-31407-0_17 · Cited by 1
The use of weather nowcasting convolutional neural network extrapolators in cardiac PET imaging
Sloka, S. (2023) · Journal of Medical Radiation Sciences · doi:10.1002/jmrs.701 · Cited by 1
“12,25 – 28 A Sigma of 25 was used for the noise estimate for BM3D.”
MethodOptimization-Inspired Deep Network for Image Restoration from Partial Random Samples
Dong, Y., Zhao, R. Q., Xiong, R., et al. (2023) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas46773.2023.10182150 · Cited by 1
“Several attempts [10], [11] are made to solve the RRS problem with neural networks.”
BackgroundMethodIDP: Image Denoising Using PoolFormer
Yin, S.-K., & Chen, J. (2023) · International Symposium on Computer, Consumer and Control · doi:10.1109/is3c57901.2023.00019 · Cited by 1
Zhang, J., Zhao, S., Hu, L., et al. (2023) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai59109.2023.00126 · Cited by 1
“Several state-of-the-art SR methods have been selected, including: A+ [22], Bicubic, CRC [23], IRCNN [24], DPIR [25], Real-ESRGAN [14].”
MethodLi, G., Zhou, Z., & Wang, G. (2024) · IET Image Processing · doi:10.1049/ipr2.13030 · Cited by 1
Style-Infused Image Restoration with GAN
Gao, J., & Su, X. (2023) · International Conference on Computer Engineering and Intelligent Control (ICCEIC) · doi:10.1109/icceic60201.2023.10426692 · Cited by 1
“With the development of neural networks, they gradually found application in image restoration [5][6] .”
BackgroundHybrid System for Image Restoration
kasim, G. M. T., Alkattan, Z. M., & Mohammed, N. M. (2024) · International Research Journal of Innovations in Engineering and Technology · doi:10.47001/irjiet/2024.801020 · Cited by 1
Efficient image restoration with style-guided context cluster and interaction
Qiao, F., Zhu, Y., & Meng, M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09440-4 · Cited by 1
“The compared methods include BRDNet [26], FOC-Net [66], RNAN [38], RDN [25], DeamNet [67], Restor-mer [68], IRCNN [1], FFDNet [21], DSNet [23], and RPCNN [69].”
Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
Jiang, X., Xu, S., Wu, J., Zhou, C., & Ji, S. (2024) · Applied Sciences · doi:10.3390/app14051742 · Cited by 1
A Deep Learning-based Data Usability Enhancement Scheme for Differential Privacy
Yan, H., Li, X., Zheng, G., et al. (2023) · IEEE Smart World Congress (SWC) · doi:10.1109/swc57546.2023.10449008 · Cited by 1
“To balance accuracy and speed, Zhang et al. [27] proposed a 7-layer denoising network with dilated convolutions [28], which expands the receptive field of CNNs. Othernotable denoising networks include the fully convolutional encoding-decoding network proposed by Mao et al. [29], which uses…”
BackgroundA deep encoder-decoder based primal-dual proximal network for image restoration
Wang, S., Jiu, M., Chen, L., Li, S., & Xu, M. (2024) · International Conference on Graphics and Image Processing (ICGIP) · doi:10.1117/12.3021256 · Cited by 2
“IRCNN[14] uses the learned CNN denoiser as a modular component of model-based optimization strategy to solve other inverse problem.”
MethodImage Denoising using Wavelet Transformer
Akhade, K., Ghodekar, S., Kapse, V., et al. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24apr1565 · Cited by 1
A Dual-Branch Self-Boosting Network Based on Noise2Noise for Unsupervised Image Denoising
Geng, Y., Xu, S., Xiong, M., et al. (2024) · Applied Sciences · doi:10.3390/app14114735 · Cited by 1
GFFNet: An Efficient Image Denoising Network with Group Feature Fusion
Gao, L., Zhang, Y., Jin, X., Qin, X., Sun, Z., & Wang, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5600-1_8 · Cited by 1
Image denoising based on nature-inspired optimized deep neural network
Vijay, M., Nandhini, N., Kavitha, K., Divya, B., & Nandhika, G. (2024) · AIP conference proceedings · doi:10.1063/5.0217549 · Cited by 1
Auras, A., Gandikota, K. V., Dröge, H., & Moeller, M. (2024) · GAMM-Mitteilungen · doi:10.1002/gamm.202470003 · Cited by 3
“[205, 9, 147] model patch-based priors based on Gaussian mixture models, convolutional neural networks or normalizing flows.”
MethodZhou, Z., Chen, Y., & Zhou, Y. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3449924 · Cited by 2
“We compare the proposed method with the classical method (i.e. CBM3D), quaternion-based methods (i.e. QCNN [22], QDL [26], QWNNM [24], and DLRQP [25]), and recently proposed deep learning methods (i.e. BRDNet [32], DnCNN [17], IRCNN [33], FFDNet [34], ADNet [35], DCDicL [9], and HyperRes [36]).”
Complementary Transformer Network for cross-scale single image denoising
Zhang, M., Liu, X., Liu, H., & Hu, J. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.08.073 · Cited by 1
Ai, L., Shi, Y., Yao, R., & Li, L. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad8294 · Cited by 1
Chen, L. T., & Xia, X. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad913b · Cited by 1
Enhancing deep image prior with roughly clean pairs and spatially random sampling
Xu, S., Xiong, M., Zhou, C., et al. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03624-0 · Cited by 1
“Our proposed method underwent a comparison with state-of-the-art methods, including BM3D [37], DnCNN [11], FFDNet [12], DAGL[38],Restormer[17],SwinIR[16],DIP[39],DRUNet [40], and IRCNN [41].”
Boosting Stereo Image Noise Removal by Learning Uncertainty and Enriched Features
Wei, B., Liu, H., Qian, C., et al. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890378 · Cited by 1
Quaternion CNN With Salient Features for Color Image Denoising
Liu, Y., Jin, Q., & Yang, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890612 · Cited by 1
“Additionally, numerous state-of-the-art denoising algorithms based on CNNs have been developed, including IRCNN [18], FFDNET [19], DudeNet [20], AirNet [21], MWDCNN [22], VIRNet [23], DRANet [24], and WACAFRN [25], among others.”
PIV analysis of flow-type landslides under suboptimal image conditions
Zuccarini, A., Vasu, N. N., Banks, V., et al. (2025) · Landslides · doi:10.1007/s10346-025-02498-8 · Cited by 1
“…including deconvolution algorithms (Lucy 1974; Hosseini & Plataniotis 2020; Satish et al. 2020) and more sophisticated machine learning approaches (Zhang et al. 2017; Lian & Wang 2023; Chen et al. 2024), the described methodology was applied directly to the unprocessed camera footage,…”
Multi-Scale Feature Fusion for Low-light Image Enhancement in the RAW Domain
Wang, W., & Song, B. (2024) · International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc62609.2024.10942102 · Cited by 1
“Therefore, combining the advantages of CNN [16], [17] and Transformer [14], [15] to construct a model that can efficiently extract both multi-scale local and global features becomes the key to improve the effectiveness of low-light image recovery.”
Mixed noise removal via generalized gaussian scale mixture modeling and plug-and-play deep prior
Xu, H., Lu, J., Weng, S., & Li, F. (2025) · Numerical Algorithms · doi:10.1007/s11075-025-02103-y · Cited by 1
Mao, X., Liu, Y., Qiu, C., et al. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3573586 · Cited by 1
“…Denoising (FFDNet) [50], Toward Convolutional Reference-free Denoising of Real Photographs (CBDNet) [40], Learning Deep Denoising Priors (IRCNN) [51], Real Image Denoising with Feature Attention (RIDNet) [52], CNN-Guided Filter for SAR Denoising (CNN-GFF) [53], CNN and Consistent Circular…”
CCNet: A Cross‐Channel Enhanced CNN for Blind Image Denoising
Zhu, M., & Xu, Z. (2025) · Computational Intelligence · doi:10.1111/coin.70063 · Cited by 1
“…standard and compare our model with several state-of-the-art models, such as BM3D [17], WNNM [39], DnCNN [19], image restoration CNN (IRCNN) [21], FFDNet [20], attention-guided denoising convolutional neural network (ADNet) [22], fractional…”
Sivanesan, T. M., & Vijayaraj, N. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425520159 · Cited by 1
DRGNet: Enhanced VVC Reconstructed Frames Using Dual-Path Residual Gating for High-Resolution Video
Gai, Z., Das, T., & Choi, K. (2025) · Sensors · doi:10.3390/s25123744 · Cited by 1
Towards Physics-Informed Cyclic Adversarial Multi-PSF Lensless Imaging
Banerjee, A., & Singh, S. K. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3579385 · Cited by 1
“Experimentsattheintersectionofclassicalmethodologiesand deep learning have garnered considerable attention [23], [24], [25].”
Sheikhha, F. H., Seo, J., & Lee, H. (2025) · Applied Sciences · doi:10.3390/app15137595 · Cited by 1
Nasrat, A. F., Çağlıkantar, T., & Barışçı, N. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07742-5 · Cited by 1
“…illustrating that AFF prioritizes perceptually significant content for noise suppression Page 22 of 44 [4], TNRD [64], DnCNN [6], FFDNet [7], IRCNN [65], ADNet [15], DudeNet [14], DRANet [54], and Restormer [35], demonstrating its superior ability to remove noise while preserving critical image…”
Learning Parametric Sparse Models for Heavy Noisy Removal From Images
Xie, X., Li, Y., Dong, W., Shi, G., Li, X., & Zheng, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2813969 · Cited by 1
Recovering Texture of Denoised Image via its Statistical Analysis
Saito, Y., & Miyata, T. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451266 · Cited by 1
“On the other hand, denoising methods using deep neural network (for example, [13, 14]) achieve about +0.”
MethodA Multi-level Aggregated Network for Image Restoration
Ma, C., Tan, W., Bare, B., et al. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00202 · Cited by 1
“, IRCNN [19], DnCNN-B, and MemNet) on Set12 and BSD68.”
BackgroundLópez-Tapia, S., Lucas, A. M., Molina, R., et al. (2019) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2019.8902338 · Cited by 1
“7836 IRCNN [22] 34.”
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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