关键词: 稀疏表示, 变量分裂, 交替方向乘子, 去模糊

Abstract: Traditional regularized image de-blurring approach is easy to produce “artifact” effect like ringing and edge blur. In order to solve this problem, a sparsity regularized image deblurring model is proposed to restore the blurry image. The gross error function between estimation image and blurred image is used as data fidelity term and non-smooth regularization term is used for constraining the sparse representation coefficients over the sparse dictionary. A non-negative term is also added to ensure the positivity of the restored image. Furthermore, inspired from the alternating direction multiplier method, a multi-variable splitting fast iterative algorithm is proposed to solve the deblurring model numerically. The original problem is transformed into solving three simple sub-problems iteratively, thus computational complexity is decreased rapidly. Comprehensive experiments demonstrate that the proposed model and its numerical algorithm maintain the structure characteristics and smoothness of the image, and reduce the computational complexity.

Key words: sparse representation, variable splitting, alternating direction multiplier method, image deblurring