Curvelet-Based Iterative Regularization and Inverse Scale Space Methods  

作　　者：LIU Guojun FENG Xiangchu 

机构地区：[1]Department of Applied Mathematics, Xidian University, Xi＇an 710071, China [2]School of Mathematics and Computer Science, Ningxia University, Yinchuan 750021, China

出　　处：《Chinese Journal of Electronics》2010年第3期548-552,共5页电子学报（英文版）

基　　金：This work is supported by the National Natural Science Foundation of China （No.60872138）.

摘　　要：For regularization theory of inverse problem in image processing, a challenge is to find a proper space in which the image is well characterized and hence restorable faithfully. Therefore, one contribution of this paper is to propose a novel variational regularization model with the help of decomposition space theory. Furthermore, as an development of it two models for image denoising are given, namely, Curvelet-based Iteratlve regularizatlon method （C-IRM） and Inverse scale spaces （C-ISS） method. Finally, experimental results on some standard test images as well as comparisons with some available methods show that the proposed methods work well in image edge preservation while achieving pleasant performance in terms of Signal-to-noise ratio （SNP,）.

关 键 词：CURVELETS Decomposition spaces De- noising Iterative regularization Inverse scale spaces Bregman distance Shrinkage. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] Q987[自动化与计算机技术—计算机科学与技术]