Paper Info
Title
Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications.
Abstract
Alternating direction method of multiplier (ADMM) is a widely used algorithm for solving constrained optimization problems in image restoration. Among many useful features, one critical feature of the ADMM algorithm is its modular structure which allows one to plug in any off-the-shelf image denoising algorithm for a subproblem in the ADMM algorithm. Because of the plug-in nature, this type of ADMM algorithms is coined the name "Plug-and-Play ADMM". Plug-and-Play ADMM has demonstrated promising empirical results in a number of recent papers. However, it is unclear under what conditions and for what denoising algorithms would it guarantee convergence. Also, it is unclear to what extent would Plug-and-Play ADMM be compared to existing methods for common Gaussian and Poissonian image restoration problems. In this paper, we propose a Plug-and-Play ADMM algorithm with provable fixed point convergence. We show that for any denoising algorithm satisfying a boundedness criteria, called bounded denoisers, Plug-and-Play ADMM converges to a fixed point under a continuation scheme. We demonstrate applications of Plug-and-Play ADMM on two image restoration problems including single image super-resolution and quantized Poisson image recovery for single-photon imaging. We compare Plug-and-Play ADMM with state-of-the-art algorithms in each problem type, and demonstrate promising experimental results of the algorithm.
Year
Venue
DocType
2016
CoRR
Journal
Volume
Citations 
PageRank 
abs/1605.01710
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Stanley H. Chan140330.95
Xiran Wang200.68
Omar A. Elgendy3644.68