Restoration of Poissonian Images Using Alternating Direction Optimization

TL;DR

This paper introduces an ADMM-based approach for restoring Poissonian images, effectively handling complex regularizers like total variation and frame-based methods, leading to improved speed and accuracy.

Contribution

It develops a novel ADMM algorithm for Poisson image deconvolution with various regularizers, providing convergence guarantees and outperforming existing methods.

Findings

The proposed method converges under certain conditions.

It outperforms state-of-the-art methods in speed.

It achieves higher restoration accuracy.

Abstract

Much research has been devoted to the problem of restoring Poissonian images, namely for medical and astronomical applications. However, the restoration of these images using state-of-the-art regularizers (such as those based on multiscale representations or total variation) is still an active research area, since the associated optimization problems are quite challenging. In this paper, we propose an approach to deconvolving Poissonian images, which is based on an alternating direction optimization method. The standard regularization (or maximum a posteriori) restoration criterion, which combines the Poisson log-likelihood with a (non-smooth) convex regularizer (log-prior), leads to hard optimization problems: the log-likelihood is non-quadratic and non-separable, the regularizer is non-smooth, and there is a non-negativity constraint. Using standard convex analysis tools, we present sufficient conditions for existence and uniqueness of solutions of these optimization problems, for several types of regularizers: total-variation, frame-based analysis, and frame-based synthesis. We attack these problems with an instance of the alternating direction method of multipliers (ADMM), which belongs to the family of augmented Lagrangian algorithms. We study sufficient conditions for convergence and show that these are satisfied, either under total-variation or frame-based (analysis and synthesis) regularization. The resulting algorithms are shown to outperform alternative state-of-the-art methods, both in terms of speed and restoration accuracy.