关键词: 图像复原, 交替优化, 稀疏表示, 正则化, 迭代重加权方法

Abstract: For rapidly and accurately restoring images, a novel image restoration algorithm is presented. In the framework of sparse representation, a new constrained optimization model is created, which enables the estimations of the original image and its sparse representation. The objective function of the model has [L1-L2] regularized terms, thus the alternating optimization method is introduced to decompose the model into equivalent sub-problems. The non-differentiable one among sub-problems is handled by iterative reweighted method. The experimental results demonstrate that with only a few iterations, the presented algorithm can achieve optimal estimations of the original image and its sparse representation. Compared with some state-of-the-art algorithms, the presented algorithm shows to be faster, and obtains better results.

Key words: image restoration, alternating optimization, sparse representation, regularization, iteratively reweighted method