[1]郭青青,周飞飞,李雷. 基于模糊裁剪阈值的SAMP压缩感知算法[J].计算机技术与发展,2017,27(09):35-39.
　GUO Qing-qing,ZHOU Fei-fei,LI Lei. Sparsity Adaptive Matching Pursuit Algorithm for Compressed Sensing with Fuzzy Pruning Threshold[J].,2017,27(09):35-39.
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 基于模糊裁剪阈值的SAMP压缩感知算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年09期
页码:
35-39
栏目:
智能、算法、系统工程
出版日期:
2017-09-10

文章信息/Info

Title:
 Sparsity Adaptive Matching Pursuit Algorithm for Compressed Sensing with Fuzzy Pruning Threshold
文章编号:
1673-629X（2017)09-0035-05
作者:
 郭青青; 周飞飞; 李雷
 南京邮电大学 理学院
Author(s):
 GUO Qing-qing；ZHOU Fei-fei；LI Lei
关键词:
 ; 压缩感知; 重构算法; 高频子带小波变换; 模糊裁剪阈值SAMP算法
Keywords:
 ; compressed sensing; reconstruction algorithm; high frequency sub-band wavelet transform; fuzzy pruning threshold sparsity a-daptive matching pursuit
分类号:
TP301.6
文献标志码:
A
摘要:
 稀疏度自适应匹配追踪(SAMP)算法是压缩感知(CS)中一种主流的图像重构算法.随着迭代次数的增加,SAMP算法的原子候选集将成倍增加,会导致系统空间的浪费和重构时间的增长.为此,提出了一种模糊裁剪阈值稀疏度自适应匹配追踪(FPTSAMP)算法.由于离散小波变换(DWT)在CS稀疏处理过程中破坏了低频逼近系数间的相关性,对信号的重构质量产生了一定的负面影响,因而采用小波高频子带变换(HFSBWT)来替代DWT,实现对信号的稀疏表示.仿真实验结果表明,相比于同一重构算法,采用HFSBWT方法得到的峰值信噪比更好;与SAMP算法相比,与HFSBWT相结合的FPTSAMP算法的重构效果有了明显提高,重构时间也减少了一半.
Abstract:
 Sparsity Adaptive Matching Pursuit ( SAMP) is a mainstream image reconstruction algorithm in Compressed Sensing ( CS) . However,with the increase of iterative times,it has multiplied atoms candidate set that lead to wasting storage capacities and lengthening reconstruction time. A method called Fuzzy Pruning Threshold Sparsity Adaptive Matching Pursuit ( FPTSAMP) is proposed. The Dis-crete Wavelet Transform ( DWT) destroys the correlation among low-frequency approximation coefficients in CS sparsity processing, which results in bad reconstruction quality,so a High Frequency Sub-Band Wavelet Transform ( HFSBWT) is adopted instead of DWT to realize the sparse representation of signal. Simulation results show that compared with the same reconstruction algorithms the HFSBWT has achieved a better Peak Signal To Noise Ratio ( PSNR) of images and that compared with SAMP algorithm the FPTSAMP combined with HFSBWT has lifted the reconstruction performance of images significantly with its reconstruction time cutting in half.

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更新日期/Last Update: 2017-10-19