Inexact Alternating Direction Method Based on Newton descent algorithm with Application to Poisson Image Deblurring

TL;DR

This paper introduces an inexact alternating direction method based on Newton descent for Poisson image deblurring, leveraging proximal Hessian information to improve convergence and outperform existing algorithms.

Contribution

It proposes a novel inexact ADMM algorithm using proximal Hessian information inspired by Newton's method, with proven global convergence for Poisson image deblurring.

Findings

Outperforms current state-of-the-art algorithms in numerical experiments

Demonstrates global convergence under certain conditions

Effectively preserves edges in Poisson image restoration

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 involve 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.