多尺度自适应直接信息采样与重构
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摘要
现有的直接信息采样(Analog-to-Information Conversion,AIC)方法在压缩感知框架下,将采集和压缩过程融合,但并未充分考虑不同稀疏成分在信号重构当中的地位。针对该不足之处,提出一种多尺度自适应直接信息采样与重构算法。该算法充分考虑小波变换后的高频信号和低频信号在重构中的不同地位,实现速率自适应的直接信息采样。同时,给出变速率采样下的信号重构策略,以解决常规重构算法在速率自适应采样时失效的问题。仿真结果表明,多尺度自适应AIC系统可以获得比传统AIC系统更好的AFSNR性能。
In the analog-to-information conversion(AIC) system within the compressed sensing framework, the sparsity of input plays an important role on its precise reconstruction. But most signals in our daily lives are not absolutely sparse but approximately sparse or compressible ones, so a method of multiscale adaptive analog-to-information conversion and reconstruction was proposed, and the validity was proved in this paper. By introducing orthogonal wavelet matrix(OWM) in the recovery end equivalently, multiscale adaptive AIC considered the different roles of coarse coefficients and detail ones in the reconstruction after wavelet transform and realized the rate adaptive sampling. Meanwhile, the adaptive orthogonal matching pursuit(AOMP) algorithm was proposed to solve the problem that the conventional reconstruction algorithm fails in rate adaptive sampling. The simulation results show that the improved AIC system could get better AFSNR performance than conventional AIC system.
In the analog-to-information conversion(AIC) system within the compressed sensing framework, the sparsity of input plays an important role on its precise reconstruction. But most signals in our daily lives are not absolutely sparse but approximately sparse or compressible ones, so a method of multiscale adaptive analog-to-information conversion and reconstruction was proposed, and the validity was proved in this paper. By introducing orthogonal wavelet matrix(OWM) in the recovery end equivalently, multiscale adaptive AIC considered the different roles of coarse coefficients and detail ones in the reconstruction after wavelet transform and realized the rate adaptive sampling. Meanwhile, the adaptive orthogonal matching pursuit(AOMP) algorithm was proposed to solve the problem that the conventional reconstruction algorithm fails in rate adaptive sampling. The simulation results show that the improved AIC system could get better AFSNR performance than conventional AIC system.
引文
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[5]XIA SENLIN,SUN HUAIJIANG,CHEN BEIJIA.A regularized tensor decomposition method with adaptive rank adjustment for compressed-sensed-domain background subtraction[J].Signal Processing:Image Communication,2018,62:149-163.
[6]ZHENG SHUAI,CHEN JIAN and KUO YONGHONG.An improved distributed compressed video sensing scheme in reconstruction algorithm[J].Multimedia Tools and Applications,2018,77(7):8711-8728.
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[10]VERHELST M,BAHAI A.Where analog meets digital:analog-to-information conversion and beyond[J].IEEESolid-State Circuits Magazine,2015,7(3):67-80.
[11]TROPP JOEL A,LASKA JASON N,DUARTE MARCOF,et al.Beyond nyquist:efficient sampling of sparse bandlimited signals[J].IEEE Transactions on Information Theory,2010,56(1):520-544.
[12]CHEN HAORAN,SUN YANFENG,GAO JUNBIN,et al.Fast optimization algorithm on Riemannian manifolds and its application in low-rank learning[J].Neurocomputing,2018,291:59-70.
[13]KUTYNIOK GITTA,LIM WANG Q.Optimal compressive imaging of fourier data[J].SIAM Journal on Imaging Sciences,2018,11(1):507-546.
[14]BARANIUK RICHARD G,GOLDSTEIN THOMAS,SANKARANARAYANAN ASWIN C,et al.Compressive video sensing:algorithms,architectures,and applications[J].IEEE Signal Processing Magazine,2017,34(1):52-66.
[15]LI GANG,WIMALAJEEWA THAKSHILA,VARSH-NEY PRAMOD K.Decentralized and collaborative subspace pursuit:a communication-efficient algorithm for joint sparsity pattern recovery with sensor networks[J].IEEE Transactions on Signal Processing,2016,64(3):556-566.
[16]TESTA MATTEO,MAGLI ENRICO.Compressive bayesian K-SVD[J].Signal Processing:Image Communication,2018,60:1-5.
[17]田文飚,芮国胜,张海波,等.压缩感知框架下非均匀信息采集及重构[J].吉林大学学报:工学版,2013,44(4):1209-1214.TIAN WENBIAO,RUI GUOSHENG,ZHANG HAIBO,et al.Non-uniform information acquisition and its reconstruction within the compressed sensing framework[J].Journal of Jilin University:Engineering and Technology Edition,2013,44(4):1209-1214.(in Chinese)
[18]WANG NING,BAO JIANWEN,LEE JINLUEN,et al.Wavelet compression technique for high-resolution global model data on an icosahedral grid[J].Journal of Atmospheric and Oceanic Technology,2015,32(9):1650-1667.