By Topic

A Generalized Accelerated Proximal Gradient Approach for Total-Variation-Based Image Restoration

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wangmeng Zuo ; Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China ; Zhouchen Lin

This paper proposes a generalized accelerated proximal gradient (GAPG) approach for solving total variation (TV)-based image restoration problems. The GAPG algorithm generalizes the original APG algorithm by replacing the Lipschitz constant with an appropriate positive-definite matrix, resulting in faster convergence. For TV-based image restoration problems, we further introduce two auxiliary variables that approximate the partial derivatives. Constraints on the variables can easily be imposed without modifying the algorithm much, and the TV regularization can be either isotropic or anisotropic. As compared with the recently developed APG-based methods for TV-based image restoration, i.e., monotone version of the two-step iterative shrinkage/thresholding algorithm (MTwIST) and monotone version of the fast IST algorithm (MFISTA), our GAPG is much simpler as it does not require to solve an image denoising subproblem. Moreover, the convergence rate of O(k-2) is maintained by our GAPG, where k is the number of iterations; the cost of each iteration in GAPG is also lower. As a result, in our experiments, our GAPG approach can be much faster than MTwIST and MFISTA. The experiments also verify that our GAPG converges faster than the original APG and MTwIST when they solve identical problems.

Published in:

IEEE Transactions on Image Processing  (Volume:20 ,  Issue: 10 )