%0 Journal Article
%A FENG Xiang-chu
%A LIU Xin
%A WANG Wei-wei
%A YANG Chun-yu
%T <i>L</i><sub>1</sub>-Nonlocal Means Regularization Model for Image Deblurring Problem
%D 2018
%R 10.13190/j.jbupt.2017-101
%J Journal of Beijing University of Posts and Telecommunications
%P 81-87
%V 41
%N 1
%X An <i>l</i><sub>1</sub>-nonlocal means regularization model was proposed in order to preserve the edges and details while deblurring the blurred image. Firstly, the article empirically gave out that the distribution of the residual in the nonlocal means denoising algorithm (differences between the noisy image and the denoised result) is heavy-tailed, which well fits the Laplacian distribution. Based on this observation, a new regularization model was proposed by using the <i>l</i><sub>1</sub>-norm constrained residual as the new regularization term. Then the corresponding optimization algorithm was designed by utilizing the Bregmanized operator splitting algorithm, which can be regarded as an extension of plug-and-play Priors algorithm. Experiments show that the new model achieves better performance than the <i>l</i><sub>2</sub>-nonlocal means regularization model and the plug-and-play priors model in terms of both restoration results and preserving the edges and details of the image.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2017-101