NDLI: Inexact alternating direction method based on Newton descent algorithm with application to Poisson image deblurring

Content Provider	Springer Nature Link
Author	Chen, Dai Qiang
Copyright Year	2016
Abstract	The recovery of images from the observations that are degraded by a linear operator and further corrupted by Poisson noise is an important task in modern imaging applications such as astronomical and biomedical ones. Gradient-based regularizers involving the popular total variation semi-norm have become standard techniques for Poisson image restoration due to its edge-preserving ability. Various efficient algorithms have been developed for solving the corresponding minimization problem with non-smooth regularization terms. In this paper, motivated by the idea of the alternating direction minimization algorithm and the Newton’s method with upper convergent rate, we further propose inexact alternating direction methods utilizing the proximal Hessian matrix information of the objective function, in a way reminiscent of Newton descent methods. Besides, we also investigate the global convergence of the proposed algorithms under certain conditions. Finally, we illustrate that the proposed algorithms outperform the current state-of-the-art algorithms through numerical experiments on Poisson image deblurring.
Starting Page	89
Ending Page	96
Page Count	8
File Format	PDF
ISSN	18631703
Journal	Signal, Image and Video Processing
Volume Number	11
Issue Number	1
e-ISSN	18631711
Language	English
Publisher	Springer London
Publisher Date	2016-09-19
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image deblurring Newton descent method Inexact alternating direction method Total variation Poisson noise Signal,Image and Speech Processing Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Multimedia Information Systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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