L1-L2 Optimization in Signal and Image Processing

By Michael Zibulevsky, Michael Elad. IEEE Signal Processing Magazine, 2010

463 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/06f7a4af7e49721c55b40065010dd27b76f485ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Lei Zhang
2. Guangming Shi
3. Weisheng Dong
4. Wangmeng Zuo
5. Xin Li
6. Zongben Xu
7. Jian Sun
8. Kyle A. Gallivan

In Computer Vision and Pattern Recognition

1. Lei Zhang
2. Wangmeng Zuo
3. Ming-Hsuan Yang
4. Luc Van Gool
5. Guangming Shi
6. Weisheng Dong
7. Dacheng Tao
8. Radu Timofte

In Signal Processing

1. Guangming Shi
2. Huibin Li
3. Kyle A. Gallivan
4. Laurent Condat
5. Yan Yang
6. Jian Sun
7. Xin Li
8. Weisheng Dong

Citations in the corpus

• ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing (2020, background): “[35], or combined regularization in some of these transform domains [29], [36].”
• Fast L1-Minimization Algorithms For Robust Face Recognition (2010, unknown): “…several works exist in the literature which attempt to provide comprehensive reviews on the performance of (cid:96) 1 -min algorithms [14], [15], [16], in addition to various amount of comparison experiments conducted in each individual paper that introduces new methods to (cid:96) 1 -min…”
• Nonlocally Centralized Sparse Representation for Image Restoration (2013, background): “(2), such as iterative thresholding algorithms [9]–[11] and Bregman split algorithms [24], [25].”
• Sparsity-based image denoising via dictionary learning and structural clustering (2011, background): “Extensive studies have been done on the design/learning of dictionary [2] and computationally efficient/robust algorithms for solving the above convex optimization problem [11].”
• A Direct Algorithm for 1-D Total Variation Denoising (2013, background): “Until not so long ago, suchmethods applied to TV regularization had rather high computational complexity [12]–[16], but the growing interest for related -norm problems in compressed sensing or sparse recovery [17], [18] has yielded advances in the field.”
• Compressive Sensing via Nonlocal Low-Rank Regularization (2014, background): “Conventional CS recovery exploits the l1-norm based sparsity of a signal and the resulting convex optimization problems can be efficiently solved by the class of surrogate-function based methods [7], [8], [9].”
• A compressed sensing approach for partial differential equations with random input data (2012, methodology): “18) such as projected gradient techniques [20, 45], Interior Points methods (IP) [48], or iterative-shrinkage techniques, see [52].”