Sparsity-based image denoising via dictionary learning and structural clustering

By Weisheng Dong, Xin Li, Lei Zhang, Guangming Shi. Computer Vision and Pattern Recognition, 2011

519 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/04f32849ecd491156feb1b56a4cd4e60d109d256

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. Xuelong Li
5. Dacheng Tao
6. Xin Li
7. Xinbo Gao
8. Nick Barnes

In Computer Vision and Pattern Recognition

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

In Media Technology

1. Dacheng Tao
2. Xuelong Li
3. Saeed Anwar
4. Nick Barnes
5. Ling Shao
6. Guangming Shi
7. Weisheng Dong
8. Yan Liu

Citations in the corpus

• Compressive Sensing via Nonlocal Low-Rank Regularization (2014, methodology): “Exploiting the so-called nonlocal self-similarity has led to the well-known nonlocal means methods [10], block-matching 3D denoising [21] and simultaneous sparse coding (SSC) [20].”
• Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation (2016, methodology): “Second, instead of using a set of orthogonal PCA basis as in [28], a non-orthogonal dictionary is learned; such relaxation is beneficial to improve the accuracy of sparse reconstruction.”
• Deep Generalized Unfolding Networks for Image Restoration (2022, background): “, total variation [49], sparse representation [15, 18, 42], low-rank [23], and self-similarity [7,12].”
• Real Image Denoising With Feature Attention (2019, methodology): “Dictionary-based methods [25, 43, 22] enforce sparsity by employing self-similar patches and learning overcomplete dictionaries from clean images.”
• Denoising Prior Driven Deep Neural Network for Image Restoration (2018, background): “Indeed, the IR community has witnessed a flurry of sparsity-based IR methods [1], [3], [11], [15] in the past decade.”
• From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms (2014, methodology): “All the other methods that involve iterations run quite slowly (MSKR, LSSC, CSR).”
• Blending Anti-Aliasing into Vision Transformer (2021, methodology): “Notably, based on the observations that the “jagged” phenomenon varies across images and locations, we propose an adaptive filtering method which draws inspirations from dictionary learning [11].”
• Image Super-Resolution With Sparse Neighbor Embedding (2012, background): “, image recovery [15], [44], pattern recognition [31], and compressed sensing [32].”