The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach
Abstract
Simultaneous sparse coding (SSC) or nonlocal image representation has shown great potential in various low-level vision tasks, leading to several state-of-the-art image restoration techniques, including BM3D and LSSC. However, it still lacks a physically plausible explanation about why SSC is a better model than conventional sparse coding for the class of natural images. Meanwhile, the problem of sparsity optimization, especially when tangled with dictionary learning, is computationally difficult to solve. In this paper, we take a low-rank approach toward SSC and provide a conceptually simple interpretation from a bilateral variance estimation perspective, namely that singular-value decomposition of similar packed patches can be viewed as pooling both local and nonlocal information for estimating signal variances. Such perspective inspires us to develop a new class of image restoration algorithms called spatially adaptive iterative singular-value thresholding (SAIST). For noise data, SAIST generalizes the celebrated BayesShrink from local to nonlocal models; for incomplete data, SAIST extends previous deterministic annealing-based solution to sparsity optimization through incorporating the idea of dictionary learning. In addition to conceptual simplicity and computational efficiency, SAIST has achieved highly competent (often better) objective performance compared to several state-of-the-art methods in image denoising and completion experiments. Our subjective quality results compare favorably with those obtained by existing techniques, especially at high noise levels and with a large amount of missing data.
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Cited by
Weighted Nuclear Norm Minimization with Application to Image Denoising
Gu, S., Zhang, L., Zuo, W., & Feng, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.366 · Cited by 2,316
Hyperspectral Image Restoration Using Low-Rank Matrix Recovery
Zhang, H., He, W., Zhang, L., Shen, H., & Yuan, Q. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2284280 · Cited by 887
Group-Based Sparse Representation for Image Restoration
Zhang, J., Zhao, D., & Gao, W. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2323127 · Cited by 768
Learning Enriched Features for Fast Image Restoration and Enhancement
Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., Yang, M.-H., & Shao, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3167175 · Cited by 531
Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation
He, W., Zhang, H., Zhang, L., & Shen, H. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2398433 · Cited by 269
Metadata
- Authors
- Dong, W., Shi, G., & Li, X.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2013-02
- Volume
- 22
- Issue
- 2
- Pages
- 700-711
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2014311222
- Cited by
- 655
- References
- 58
- FWCI
- 23.28
- Type
- journal article
- Language
- en
- Open access
- closed
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