Solving total-variation image super-resolution problems via proximal symmetric alternating direction methods

DOI10.1186/s13660-016-1136-7zbMath1347.65046OpenAlexW2512967577WikidataQ59466622 ScholiaQ59466622MaRDI QIDQ308168

Bin Gao, Fenggang Sun, Shiming Xu, Ying Tong

Publication date: 5 September 2016

Published in: Journal of Inequalities and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1186/s13660-016-1136-7

zbMATH Keywords

convergencelinearizationcomputer visionconvex minimizationnumerical resultlinearized Peaceman-Rechford splitting methodproximal symmetric alternating direction method of multiplierssingle image super-resolutionstrictly contractive

Mathematics Subject Classification ID

Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

• Image super-resolution by TV-regularization and Bregman iteration
• Matrix computations.
• Superresolution image reconstruction using fast inpainting algorithms
• On the $O(1/n)$ Convergence Rate of the Douglas–Rachford Alternating Direction Method
• A Strictly Contractive Peaceman--Rachford Splitting Method for Convex Programming
• Solving Constrained Total-variation Image Restoration and Reconstruction Problems via Alternating Direction Methods
• Convergence Study on the Symmetric Version of ADMM with Larger Step Sizes
• A Strictly Contractive Peaceman-Rachford Splitting Method with Logarithmic-Quadratic Proximal Regularization for Convex Programming
• Fast Single Image Super-Resolution Using a New Analytical Solution for <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math> </inline-formula>–<inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math> </inline-formula> Problems
• Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization
• Image Super-Resolution Via Sparse Representation
• Statistical Modeling of 3-D Natural Scenes With Application to Bayesian Stereopsis
• Deconvolving Images With Unknown Boundaries Using the Alternating Direction Method of Multipliers

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