A weighted denoising method based on Bregman iterative regularization and gradient projection algorithms

DOI10.1186/s13660-017-1551-4zbMath1377.65029OpenAlexW2768019393WikidataQ47589399 ScholiaQ47589399MaRDI QIDQ1677994

Beilei Tong

Publication date: 14 November 2017

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

Full work available at URL: https://doi.org/10.1186/s13660-017-1551-4

zbMATH Keywords

optimizationtotal variationimage denoisingnumerical resultBregman distancegradient projection method

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

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• Nonlinear total variation based noise removal algorithms
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• The Split Bregman Method for L1-Regularized Problems
• Acceleration Methods for Total Variation-Based Image Denoising
• Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
• State Constraints in Convex Control Problems of Bolza
• Image Processing and Analysis
• Mathematical problems in image processing. Partial differential equations and the calculus of variations. Foreword by Olivier Faugeras

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