Fast image deconvolution using closed-form thresholding formulas of regularization-Reference-Cited by-同舟云学术

Fast image deconvolution using closed-form thresholding formulas of regularization

Published:2013-01 Issue:1 Volume:24 Page:31-41
ISSN:1047-3203
Container-title:Journal of Visual Communication and Image Representation
language:en
Short-container-title:Journal of Visual Communication and Image Representation

Author:

Cao Wenfei,Sun Jian,Xu Zongben

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Media Technology,Signal Processing

Reference25 articles.

1. Z.B. Xu, Data modeling: visual psychology approach and L12 regularization theory, in: Proceedings of the International Congress of Mathematicians, 2010, pp. 1–34.

2. L12 regularization: an iterative half thresholding algorithm;Xu;IEEE Trans. Neural Networks Learning Syst.,2012

3. Fast image deconvolution using hyper-Laplacian priors;Krishnan;Adv. Neural Inf. Process. Syst. (NIPS),2009

4. Removing camera shake from a single photograph;Fergus;ACM Trans. Graphics,2006

5. Fast motion deblurring;Cho;ACM Trans. Graphics,2009

Cited by 103 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Bisection method for computing the proximal operator of the ℓp-norm for any 0<p<1 with application to Schatten p-norms;Journal of Computational and Applied Mathematics;2024-09

2. A Levenberg–Marquardt Method for Nonsmooth Regularized Least Squares;SIAM Journal on Scientific Computing;2024-08-12

3. Efficient image restoration via non-convex total variation regularization and ADMM optimization;Applied Mathematical Modelling;2024-08

4. Impact Force Localization and Reconstruction via ADMM-based Sparse Regularization Method;Chinese Journal of Mechanical Engineering;2024-07-15

5. A fast algorithm for sparse signal recovery via fraction function;Electronics Letters;2024-06