The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Compressive Sensing via Nonlocal Low-Rank Regularization
Abstract
Sparsity has been widely exploited for exact reconstruction of a signal from a small number of random measurements. Recent advances have suggested that structured or group sparsity often leads to more powerful signal reconstruction techniques in various compressed sensing (CS) studies. In this paper, we propose a nonlocal low-rank regularization (NLR) approach toward exploiting structured sparsity and explore its application into CS of both photographic and MRI images. We also propose the use of a nonconvex log det ( X) as a smooth surrogate function for the rank instead of the convex nuclear norm and justify the benefit of such a strategy using extensive experiments. To further improve the computational efficiency of the proposed algorithm, we have developed a fast implementation using the alternative direction multiplier method technique. Experimental results have shown that the proposed NLR-CS algorithm can significantly outperform existing state-of-the-art CS techniques for image recovery.
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Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
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Image denoising by sparse 3-D transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.901238 · Cited by 9,283
Sparse MRI: The application of compressed sensing for rapid MR imaging
Lustig, M., Donoho, D., & Pauly, J. M. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21391 · Cited by 6,952
Robust principal component analysis?
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Self-citationSENSE: Sensitivity encoding for fast MRI
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A Singular Value Thresholding Algorithm for Matrix Completion
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Cited by
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
Zhang, J., & Ghanem, B. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00196 · Cited by 1,309
ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing
Yang, Y., Sun, J., Li, H., & Xu, Z. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2883941 · Cited by 908
Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision
Gu, S., Xie, Q., Meng, D., Zuo, W., Feng, X., & Zhang, L. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0930-5 · Cited by 816
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements
Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., & Ashok, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.55 · Cited by 768
Rank Minimization for Snapshot Compressive Imaging
Liu, Y., Yuan, X., Suo, J., Brady, D. J., & Dai, Q. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2873587 · Cited by 466
Metadata
- Authors
- Dong, W., Shi, G., Li, X., Ma, Y., & Huang, F.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2014-08
- Volume
- 23
- Issue
- 8
- Pages
- 3618-3632
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2018990310
- Cited by
- 598
- References
- 37
- FWCI
- 35.75
- Type
- journal article
- Language
- en
- Open access
- closed
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- Dong, W., Shi, G., Li, X., Ma, Y., & Huang, F. (2014). Compressive Sensing via Nonlocal Low-Rank Regularization. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2014.2329449
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