Inexact alternating direction method based on Newton descent algorithm with application to Poisson image deblurring. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Chen, Dai-Qiang
• Is Part Of: Signal, image and video processing. Volume 11:Issue 1(2017)
• Page Start: 89
• Page End: 96
• Publication Date: 2017-01
• Subjects: Image deblurring -- Newton descent method -- Inexact alternating direction method -- Total variation -- Poisson noise; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0973-7
• Languages: English
• ISSNs: 1863-1703

Abstract

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.