Image Sequence Denoising via Sparse and Redundant Representations

By Matan Protter, Michael Elad. IEEE Transactions on Image Processing, 2009

365 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/08253ca281c3603e0eaf3a4955fa468d42d165b6

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In Biomedical Engineering

In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

The SURE-LET Approach to Image Denoising (2007, methodology): “Up to our knowledge, the PSNRs obtained by the VBM3D are among the best published so far for video denoising [141].”
Color demosaicking by local directional interpolation and nonlocal adaptive thresholding (2011, methodology): “Recently, the sparsity prior of x has been successfully used for image restoration [32-33, 37-40, 14].”
Online Learning for Matrix Factorization and Sparse Coding (2009, neutral): “This is particularly important in the context of image and video processing (Protter and Elad, 2009; Mairal et al., 2008c), where it is common to learn dictionaries adapted to small patches, with training data that may include several millions of these patches (roughly one per pixel and per frame).”
Sparse Modeling for Image and Vision Processing (2014, methodology): “We also present natural extensions of dictionary learning to video processing [Protter and Elad, 2009], and finally, we conclude this section with a presentation of other patch-modeling approaches [Buades et al.”
Video Denoising, Deblocking, and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms (2012, background): “2199324 applications involving digital videos has motivated a significant interest in restoration or enhancement solutions, and the literature contains a plethora of such algorithms (see [3], [4] for a comprehensive overview).”
A Survey of Sparse Representation: Algorithms and Applications (2015, background): “[129] O. Bryt and M. Elad, “Compression of facial images using the k-svd algorithm,”Journal of Visual Communication and Image Representation, vol. 19, no. 4, pp. 270–282, 2008.”
Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit (2008, methodology): “In a common image processing task, for instance, the image could be decomposed into thousands of (possibly overlapping) image patches, with each patch undergoing one or more sparse-coding processes ([10, 11, 12, 13, 14] and others).”
MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning (2011, methodology): “These algorithms typically alternatebetweenfindingthedictionary ,andthesparserepresentations . In particular, the K-SVD algorithm [24] has been widely used in many applications [26]‐[ 28 ].”
Robust video denoising using low rank matrix completion (2010, background): “[22, 19]), where the denoised image patches are found by seeking for the sparsest solution in a patch dictionary.”