燕山大学学报2012，Vol.36Issue(5)：404-408,5.DOI:10.3969/j.issn.1007-791X.2012.05.006
基于Curvelet稀疏和共轭梯度法的压缩传感图像重构
Image compressed sensing reconstruction based on Curvelet-shrinkage and conjugate-gradient solution
摘要
Abstract
Compressed sensing is famous for its low-sampling rate and stronger noise-resistance. In the prior condition that image have sparse representation, it can reconstruct the original image accurately from fewer measurements of random projection. Orthogonal wavelets have bad directional selectivity. Traditional reconstruction algorithms requires big memory, has slow convergence speed, and can't balance image details and smoothing components. Aiming at this problem, a reconstruction algorithm is represented that bases on sparse representation of the image in curvelet-shrinkage transform domain and conjugate-gradient. Experiment results show that the algorithm improves the peak signal-to-noise ratio, fasters convergence speed and balances the image details and smoothing component.关键词
压缩传感/Curvelet/共轭梯度Key words
compressed sensing/ Curvelet/ conjugate-gradient分类
矿业与冶金引用本文复制引用
胡春海,赵爱罡,张海峰,张湃..基于Curvelet稀疏和共轭梯度法的压缩传感图像重构[J].燕山大学学报,2012,36(5):404-408,5.基金项目
河北省自然科学基金资助项目(F2011203117) （F2011203117）
