Inexact Alternating Direction Method Based on Newton descent algorithm with Application to Poisson Image Deblurring
This work addresses image restoration in fields like astronomy and biomedicine, but it appears incremental as it builds on existing alternating direction and Newton-based methods.
The paper tackles Poisson image deblurring by proposing an inexact alternating direction method that incorporates proximal Hessian information, reminiscent of Newton descent, and demonstrates through numerical experiments that it outperforms current state-of-the-art algorithms.
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.