燕山大学学报2016，Vol.40Issue(6)：499-507,9.DOI:10.3969/j.issn.1007-791X.2016.06.005
基于各向异性图像多尺度几何变换的压缩感知去噪算法
Denoising algorithm for compressed sensing based on anisotropic multiscale geometric transformation
摘要
Abstract
Starting from CS sparse representation and drawing on the principles of discrete shearlet filter design a new tight frame⁃work wavelet⁃based shearlet transform is constructed on anisotropic multi⁃scale image for optimal sparse representation. Using the random nature of having a local structured as a random matrix observation matrix sample the original image combined with the aforementioned sparse representation and the improved split augmented Lagrangian shrinkage threshold algorithm in the reconstruc⁃tion process as the reconstruction algorithm.Simulation results show that CS denoising reconstruction algorithm can be reconstructed from the established observation that small amounts of data from noisy images and at the same time effectively eliminate noise retain more image edges and details.关键词
压缩感知/多尺度几何变换/测量矩阵/离散剪切滤波器Key words
compressed sensing/multiscale geometric transformation/measurement matrix/discrete shearlet filter分类
信息技术与安全科学引用本文复制引用
张海锋,胡春海..基于各向异性图像多尺度几何变换的压缩感知去噪算法[J].燕山大学学报,2016,40(6):499-507,9.基金项目
国家自然科学基金资助项目 （）
