Fixed-Point-like method for a new Total variation-based image restoration model

DOI10.14317/jami.2020.519zbMath1480.94011OpenAlexW3108158805MaRDI QIDQ5027053

Jae Heon Yun, Yu Jin Won

Publication date: 3 February 2022

Full work available at URL: https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002629572

zbMATH Keywords

total variationimage restorationproximity operatorfixed-point methodsplit Bregman method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20)

Cites Work

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• Nonlinear total variation based noise removal algorithms
• Split Bregman iteration algorithm for total bounded variation regularization based image deblurring
• Modification of TV-ROF denoising model based on split Bregman iterations
• A fast fixed point algorithm for total variation deblurring and segmentation
• A proximity algorithm accelerated by Gauss–Seidel iterations for L1/TV denoising models
• The Split Bregman Method for L1-Regularized Problems
• Split Bregman Methods and Frame Based Image Restoration
• First-Order Methods in Optimization
• Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
• Weak convergence of the sequence of successive approximations for nonexpansive mappings

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