数据采集与处理2018，Vol.33Issue(1)：151-160,10.DOI:10.16337/j.1004-9037.2018.01.017
图像分块压缩感知中的自适应粒重建算法
Adaptive Granular Reconstruction in Block Compressed Sensing of Images
摘要
Abstract
In the framework of block compressed sensing(BCS),the reconstruction algorithm based on the smoothed-projected Landweber iteration can achieve better performance of rate-distortion with a low computational complexity,especially for the case using the principle component analysis(PCA)to con-duct adaptive hard-thresholding shrinkage.However,during learning PCA matrix,the reconstruction performance of Landweber iteration is affected because of neglecting the stationary local structural char-acteristic of image.To solve the above problems,the granular computing(GrC)is adopted to decompose an image into several granules depending on the structural features of patches,and then PCA is per-formed to learn the sparse representation basis corresponding to each granule.Finally,the hard-threshol-ding shrinkage is used to remove the noises in patches.The patches in granule have the stationary local structural characteristic,and the proposed method can thus effectively improve the performance of hard-thresholding shrinkage.Experimental results indicate that the reconstructed image by the proposed algo-rithm has a better objective quality when comparing with several traditional ones,and its edge and tex-ture details are better preserved,which guarantees the better subjective visual quality.Besides,the method has a low computational complexity of reconstruction.关键词
分块压缩感知/Landweber迭代/粒计算/主成分分析/硬阈值收缩Key words
block compressed sensing(BCS)/Landweber iteration/granular computing(GrC)/principle component analysis(PCA)/hard-thresholding shrinkage分类
信息技术与安全科学引用本文复制引用
李然,孙艳歌,张清洁,刘宏兵..图像分块压缩感知中的自适应粒重建算法[J].数据采集与处理,2018,33(1):151-160,10.基金项目
国家自然科学基金(61501393,61572417)资助项目 （61501393,61572417）
信阳师范学院青年科研基金(2015-QN-043)资助项目. （2015-QN-043）
