信息与控制2011，Vol.40Issue(2)：163-169,7.DOI:10.3724/SP.J.1219.2010.00163
图像复原的Contourlet收缩与广义全变分正则化方法
Contourlet Shrinkage and Generalized TV Regularization Method for Image Restoration
摘要
Abstract
A generalized total variation (TV) model is established, and the key role of regular item in restoration algorithm is analysed from aspects of the flat areas and the edge region image respectively. Image needs to be an isotropic spread in the flat areas and be the anisotropic diffusion in the edge region. This paper derives some conditions of general TV model that need to be satisfied based on the theoretical analysis of diffusion in the flat areas and the edge region. The new method of introducing Contourlet shrinkage to the regularization is provided in order to prevent recovery model failure in high-noise cases and overcome the effects of the blocking artifacts. Contourlet is multiresolution, local, multi-direction image sparse representation. Contourlet shrinkage that is introduced to the regularization item plays the role of the de-noising and extracting the important information from image. At last, Experiment results show that the new combination method of Contourlet shrinkage and generalized TV regularization takes into account the image smoothness and edge-preserving. Especially, in the image severely blurred and the more noise cases, the new model is efficient than the improved TV models.关键词
图像复原/全变分模型/方块效应/Contourlet收缩/正则化/广义全变分模型Key words
image restoration/ total variation (TV) model/ blocking artifacts/ Contourlet shrinkage/ regularization/ generalized total variation (TV) model分类
信息技术与安全科学引用本文复制引用
文乔农,万遂人,刘增力..图像复原的Contourlet收缩与广义全变分正则化方法[J].信息与控制,2011,40(2):163-169,7.基金项目
国家自然科学基金资助项目(60872157). （60872157）
