泊松噪声下图像去模糊的几个非凸模型

摘要

图像复原中通常假设图像在梯度域上是稀疏的，而非凸正则化方法会更加促进稀疏性。本文基于近年出现的几类非凸正则项，提出了泊松噪声下图像去模糊问题的几个非凸模型，发展了相应的高效求解算法，并研究了算法的收敛性；数值实验表明所提出的非凸模型可以增强图像在梯度域上的稀疏性，并优于一些现有的方法。%In image restoration, images are often assumed to be sparse after taking gradient. Nonconvex regularizers could produce more sparse gradients than convex regular-izers. In this paper, based on some recent nonconvex regularizers, we propose several nonconvex models for image deblurring under Poisson noise. We develop efficient numerical algorithms for solving the proposed models and carry out the convergence analysis. Numerical results show that the proposed models achieve an enhanced gradient sparsity and yield restoration results competitive with some existing methods.