The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Plug-and-Play Image Restoration With Deep Denoiser Prior
Open accessAbstract
Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems. Such a property induces considerable advantages for plug-and-play image restoration (e.g., integrating the flexibility of model-based method and effectiveness of learning-based methods) when the denoiser is discriminatively learned via deep convolutional neural network (CNN) with large modeling capacity. However, while deeper and larger CNN models are rapidly gaining popularity, existing plug-and-play image restoration hinders its performance due to the lack of suitable denoiser prior. In order to push the limits of plug-and-play image restoration, we set up a benchmark deep denoiser prior by training a highly flexible and effective CNN denoiser. We then plug the deep denoiser prior as a modular part into a half quadratic splitting based iterative algorithm to solve various image restoration problems. We, meanwhile, provide a thorough analysis of parameter setting, intermediate results and empirical convergence to better understand the working mechanism. Experimental results on three representative image restoration tasks, including deblurring, super-resolution and demosaicing, demonstrate that the proposed plug-and-play image restoration with deep denoiser prior not only significantly outperforms other state-of-the-art model-based methods but also achieves competitive or even superior performance against state-of-the-art learning-based methods. The source code is available at https://github.com/cszn/DPIR.
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Cites
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Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
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“The second step is to solve the prior subproblem with any off-the-shelf denois-ers, such as K-SVD [21], non-local means [22], BM3D [23].”
Bayesian-Based Iterative Method of Image Restoration*
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Zhang, K., Liang, J., Van Gool, L., & Timofte, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00475 · Cited by 760
Self-citationEfficient Long-Range Attention Network for Image Super-Resolution
Zhang, X., Zeng, H., Guo, S., & Zhang, L. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19790-1_39 · Cited by 620
Self-citationXiao, J., Fu, X., Liu, A., Wu, F., & Zha, Z.-J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183612 · Cited by 389
DiffIR: Efficient Diffusion Model for Image Restoration
Xia, B., Zhang, Y., Wang, S., Wang, Y., Wu, X., Tian, Y., Yang, W., & Van Gool, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01204 · Cited by 412
Self-citationDOLCE: 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
Metadata
- Authors
- Zhang, K., Li, Y., Zuo, W., Zhang, L., Van Gool, L., & Timofte, R.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022-10-01
- Volume
- 44
- Issue
- 10
- Pages
- 6360-6376
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3081639259
- Cited by
- 1,113
- References
- 20
- FWCI
- 53.88
- Type
- journal article
- Language
- en
- Open access
- green
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