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Claims the paper makes
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The proposed CNN denoiser with dilated convolutions achieves average PSNR 29.15 dB on BSD68 at noise level 25, better than 28.94 dB of a 7-layer network with traditional 3×3 convolution and close to 29.20 dB of a 16-layer network.
“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.”
From Learning Deep CNN Denoiser Prior for Image Restoration (2017) · cites this paper as [50]
Using a large training dataset does not improve PSNR results on BSD68 but can slightly improve performance on other testing images.
“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.”
From Learning Deep CNN Denoiser Prior for Image Restoration (2017) · cites this paper as [50]
Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986 · see citing works
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 4× · see citing works
“Instead of running the Markov chain until convergence we use the idea of contrastive divergence (Hinton, 2002) to initialize the sampler at the data points and only run it for a small, fixed number of steps.”
BackgroundMethodInfluentialRegion Filling and Object Removal by Exemplar-Based Image Inpainting
Criminisi, A., Perez, P., & Toyama, K. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.833105 · Cited by 3,258 · cited 2× · see citing works
“Other techniques have been developed that can also fill in textured areas by synthesizing or copying appropriate textures (e.g., Criminisi et al. 2004 ).”
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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2662206 · Cited by 8,983 · see citing works
Learning Deep CNN Denoiser Prior for Image Restoration
Zhang, K., Zuo, W., Gu, S., & Zhang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.300 · Cited by 2,116 · see citing works
“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.”
BackgroundMethodInfluentialFrom learning models of natural image patches to whole image restoration
Zoran, D., & Weiss, Y. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126278 · Cited by 1,522 · see citing works
Image denoising: Can plain neural networks compete with BM3D?
Burger, H. C., Schuler, C. J., & Harmeling, S. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247952 · Cited by 1,325 · see citing works
Shrinkage Fields for Effective Image Restoration
Schmidt, U., & Roth, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.349 · Cited by 543 · see citing works
Self-citationA Machine Learning Approach for Non-blind Image Deconvolution
Schuler, C. J., Burger, H. C., Harmeling, S., & Scholkopf, B. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.142 · Cited by 319 · see citing works
FlexISP: a flexible camera image processing framework
Heide, F., Steinberger, M., Tsai, Y.-T., Rouf, M., Pająk, D., Reddy, D., Gallo, O., Liu, J., Heidrich, W., Egiazarian, K., et al. (2014) · ACM Transactions on Graphics · doi:10.1145/2661229.2661260 · Cited by 298 · see citing works
Gaussian Conditional Random Field Network for Semantic Segmentation
Vemulapalli, R., Tuzel, O., Liu, M.-Y., & Chellappa, R. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.351 · Cited by 150 · see citing works
Training an Active Random Field for Real-Time Image Denoising
Barbu, A. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2028254 · Cited by 105 · see citing works
Degradation-Resistant Unfolding Network for Heterogeneous Image Fusion
He, C., Li, K., Xu, G., Zhang, Y., Hu, R., Guo, Z., & Li, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01159 · Cited by 37 · see citing works
How Well Do Filter-Based MRFs Model Natural Images?
Gao, Q., & Roth, S. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-32717-9_7 · Cited by 22 · see citing works
Self-citation
- Authors
- Roth, S., & Black, M. J.
- Affiliations
- Technische Universität Darmstadt (DE)
- Brown University (US)
- Corresponding author
- Roth, Stefan
- ORCID
- Roth, Stefan
- Venue
- International Journal of Computer Vision
- Published
- 2009-01-24
- Volume
- 82
- Issue
- 2
- Pages
- 205-229
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2130184048
- Cited by
- 847
- References
- 4
- Citations by year
- 2026: 17 · 2025: 30 · 2024: 45 · 2023: 44 · 2022: 50 · 2021: 97 · 2020: 81 · 2019: 82 · 2018: 49 · 2017: 55 · 2016: 44 · 2015: 47 · 2014: 49 · 2013: 44 · 2012: 40
- FWCI
- 41.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- International Journal of Computer Vision (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (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 › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- image priors, Markov random fields, product of experts, natural scene statistics, image denoising, image inpainting, approximate inference
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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