The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Deblurring via Stochastic Refinement
Open accessAbstract
Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics are known to be poorly correlated with human perception, and often lead to unrealistic reconstructions. We present an alternative framework for blind deblurring based on conditional diffusion models. Unlike existing techniques, we train a stochastic sampler that refines the output of a deterministic predictor and is capable of producing a diverse set of plausible reconstructions for a given input. This leads to a significant improvement in perceptual quality over existing state-of-the-art methods across multiple standard benchmarks. Our predict-and-refine approach also enables much more efficient sampling compared to typical diffusion models. Combined with a carefully tuned network architecture and inference procedure, our method is competitive in terms of distortion metrics such as PSNR. These results show clear benefits of our diffusion-based method for deblurring and challenge the widely used strategy of producing a single, deterministic reconstruction.
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Cites
Image quality assessment: from error visibility to structural similarity
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
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Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.08500 · Cited by 8,365
Enhanced Deep Residual Networks for Single Image Super-Resolution
Lim, B., Son, S., Kim, H., Nah, S., & Lee, K. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.151 · Cited by 7,736
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Mittal, A., Soundararajan, R., & Bovik, A. C. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2012.2227726 · Cited by 6,766
Image Style Transfer Using Convolutional Neural Networks
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Large Scale GAN Training for High Fidelity Natural Image Synthesis
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
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Cited by
Diffusion Models: A Comprehensive Survey of Methods and Applications
Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., & Yang, M.-H. (2024) · ACM Computing Surveys · doi:10.1145/3626235 · Cited by 1,704
(2022) · doi:10.1007/978-3-031-20053-3_27 · Cited by 1,060
Diffusion Models, Image Super-Resolution, and Everything: A Survey
Moser, B. B., Shanbhag, A. S., Raue, F., Frolov, S., Palacio, S., & Dengel, A. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3476671 · Cited by 117
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
Liu, J., Anirudh, R., Thiagarajan, J. J., He, S., Mohan, K. A., Kamilov, U. S., & Kim, H. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00963 · Cited by 106
FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model
Zhong, X., Chen, L., Liu, J., Lin, C., Qi, Y., & Li, H. (2024) · Science China Earth Sciences · doi:10.1007/s11430-023-1427-x · Cited by 37
Metadata
- Authors
- Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A. G., & Milanfar, P.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 16272-16282
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4226423379
- Cited by
- 259
- References
- 52
- FWCI
- 21.44
- Type
- conference paper
- Language
- en
- Open access
- green
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- Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A. G., & Milanfar, P. (2022). Deblurring via Stochastic Refinement. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52688.2022.01581
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