Recent Developments in Total Variation Image Restoration

By Tony F. Chan, Selim Esedoglu, Frederick E. Park, Andy M. Yip. 2004

392 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/20a787fe09f781f27741a1a4c376d064922f5e1a

How researchers see this paper

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

Citations in the corpus

Nonlocally Centralized Sparse Representation for Image Restoration (2013, methodology): “In the past decades, extensive studies have been conducted on developing various IR approaches [4-23].”
Color demosaicking by local directional interpolation and nonlocal adaptive thresholding (2011, background): “For example, in the total variational (TV) based image restoration [27-29, 31],”
Adaptive total variation image deblurring: A majorization-minimization approach (2009, methodology): “The TV regularizer (introduced in [15], see also [17] for a review of recent advances and pointers to the literature) appears in the context of bounded variation (BV) functions.”
A Direct Algorithm for 1-D Total Variation Denoising (2013, background): “Until not so long ago, such methods applied to TV regularization had rather high computational complexity [11, 12, 13, 14, 15], but the growing interest for related l1norm problems in compressed sensing, sparse recovery, or low rank matrix completion [16, 17, 18], has yielded advances in the field.”
On the Role of Sparse and Redundant Representations in Image Processing (2010, background): “This path spans from the simple `2-norm on local differences (expressing smoothness), through robust and thus edge preserving measures of smoothness, such as the total variation [12], [49].”
Restoration of Poissonian Images Using Alternating Direction Optimization (2010, unknown): “As is well known, the TV denoising problem has no closed form solution, and many iterative algorithms have be n proposed to solve it (see [4], [6], [10], [31], and reference s therein).”
Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models (2010, background): “The model immediately attracted much attention and has been extended to high order models [13, 46, 27, 29, 24, 35] and vectorial models for color image restoration [34, 2, 4, 14]; see [15] for an overview.”
A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration (2009, unknown): “, [42, 8, 10, 14].”
An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing (2010, background): “E. Candès, Tao, and Donoho prove that if the measurements satisfy the RIP of a certain degree, it is sufficient to recover the sparse signal exactly from its decoded signal.”
Sparse Reconstruction by Separable Approximation (2008, background): “Another interesting type of regularizer is the total-variation (TV) norm [64], which is of particular interest for image restoration problems [13].”
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction (2008, methodology): “Such examples arise in both image processing (e.g., [28, 8 ]) and compres-”
Fast Image Recovery Using Variable Splitting and Constrained Optimization (2009, unknown): “Arguably , the best known and most often used regularizer used in analysis pproaches to image restoration is the total variation (TV) norm [40], [11].”
Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization (2010, unknown): “…was supported by the National Science Foundation of China under Grant 60736043, Grant 61033004, Grant 61070138, and Grant 61071170, the Fundamental Research Funds of the Central Universities of China under Grant K50510020003, and the Hong Kong RGC General Research Fund under Grant PolyU 5375/09E.”