A fast algorithm for the total variation model of image denoising

DOI10.1007/s10444-009-9128-5zbMath1191.94017OpenAlexW2038133942MaRDI QIDQ983698

Hanqing Zhao, Rong-Qing Jia

Publication date: 24 July 2010

Published in: Advances in Computational Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10444-009-9128-5

zbMATH Keywords

optimizationtotal variationimage denoising

Mathematics Subject Classification ID

Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (12)

Fixed point algorithm based on adapted metric method for convex minimization problem with application to image deblurring ⋮ Nonlinear multigrid method for solving the anisotropic image denoising models ⋮ A new TV-Stokes model for image deblurring and denoising with fast algorithms ⋮ A weighted denoising method based on Bregman iterative regularization and gradient projection algorithms ⋮ A fast fixed point algorithm for total variation deblurring and segmentation ⋮ Domain decomposition methods with graph cuts algorithms for total variation minimization ⋮ Explicit iteration schemes for minimization problems arising from image denoising ⋮ Image inpainting using a complex 2-D dual-tree wavelet transform ⋮ Efficient algorithms for hybrid regularizers based image denoising and deblurring ⋮ A projection proximal-point algorithm for MR imaging using the hybrid regularization model ⋮ A projection method based on the splitting Bregman iteration for the image denoising ⋮ An adaptive algorithm for TV-based model of three norms \(L_q\) \((q = \frac{1}{2}, 1, 2)\) in image restoration

Cites Work

• Nonlinear total variation based noise removal algorithms
• Convergence analysis of the Bregman method for the variational model of image denoising
• An Iterative Regularization Method for Total Variation-Based Image Restoration
• Unnamed Item

This page was built for publication: A fast algorithm for the total variation model of image denoising