Implementation of high-order variational models made easy for image processing

DOI10.1002/mma.3858zbMath1388.94014OpenAlexW2294876837MaRDI QIDQ3187864

Li Bai, Ryan Wen Liu, Zhenkuan Pan, Zhaowen Qiu, Wenqi Lu, Jinming Duan

Publication date: 5 September 2016

Published in: Mathematical Methods in the Applied Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/mma.3858

zbMATH Keywords

total variationimage processingtotal generalised variationtotal curvaturesplit Bregman algorithmbounded Hessian

Mathematics Subject Classification ID

Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Finite difference methods for boundary value problems involving PDEs (65N06)

Related Items (10)

Blind deconvolution using bilateral total variation regularization: a theoretical study and application ⋮ A combined higher order non-convex total variation with overlapping group sparsity for Poisson noise removal ⋮ A second-order nonlocal regularized variational model for multiframe image super-resolution ⋮ Semi-blind image deblurring by a proximal alternating minimization method with convergence guarantees ⋮ A new approach for Cauchy noise removal ⋮ Image denoising based on a new anisotropic mean curvature model ⋮ Effect of image denoising on geometric moments in image applications ⋮ Adaptive image restoration via a relaxed regularization of mean curvature ⋮ Fast global minimization of the Chan-Vese model for image segmentation problem ⋮ A fractional-order regularization with sparsity constraint for blind restoration of images

Uses Software

• RecPF
• tvreg

Cites Work

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