On the Role of Sparse and Redundant Representations in Image Processing

By Michael Elad, Mário A. T. Figueiredo, Yi Ma. Proceedings of the IEEE, 2010

720 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/539346f7f35f83875d9e86851010ee35d1a47e69

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. Xuelong Li
3. David Dapeng Zhang
4. Guangming Shi
5. Weisheng Dong
6. Yong Xu
7. Jian Yang
8. Wangmeng Zuo

In Computer Vision and Pattern Recognition

1. Lei Zhang
2. Dacheng Tao
3. Wangmeng Zuo
4. Luc Van Gool
5. Ming-Hsuan Yang
6. Xuelong Li
7. Ling Shao
8. Shuicheng Yan

Citations in the corpus

• Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation (2016, background): “The class of sparsity promoting techniques [17], [18] have also been proposed for hyperspectral and multispectral image fusion, showing promising results.”
• Visual classification with multi-task joint sparse representation (2010, methodology): “We adopt the following terminology from this literature: a representation task is defined on a training set Xk = [ Xk1 , ..., X k J ] with J classes and a query data yk to be represented, where k ∈ {1, ..., K} indexes tasks.”
• Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization (2010, background): “Recently, there has been much effort in learning dictionaries from example image patches [13]–[15], [26]–[31], [55], leading to state-of-the-art”
• Nonlocally Centralized Sparse Representation for Image Restoration (2013, methodology): “As an alternative, in recent years the sparsity-based regularization [9-23] has led to promising results for various image restoration problems [1-3, 16-23].”
• Sparse Dictionary Learning for Edit Propagation of High-Resolution Images (2014, background): “More works along those lines can be found in a recent comprehensive survey [8].”
• Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling (2013, background): “In recent years, the sparse representation models (SRM) [14-29] have shown promising results in image”
• A New Pan-Sharpening Method Using a Compressed Sensing Technique (2011, methodology): “I. INTRODUCTION O PTICAL remote sensors in satellites can provide imagesabout the surface of the Earth which are valuable for environmental monitoring, land-cover classification, weather forecasting, etc. Practically, most optical Earth observation satellites, such as QuickBird and IKONOS, provide…”
• Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features (2013, background): “Various applications in computer vision, data mining, and signal processing have proven its effectiveness [31], [32].”
• Hyperspectral Image Super-Resolution via Non-local Sparse Tensor Factorization (2017, background): “The problem in (20) is a NP-hard problem [11] , which indicates that the problem should be relaxed or solved by greedy strategy.”
• A Survey of Sparse Representation: Algorithms and Applications (2015, background): “In the context of compressed sensing, it is first assumed that all the signals are sparse or approximat ely sparse enough [4, 6, 7].”