基于压缩感知的激光照明成像方法
|
摘要
针对传统的激光成像技术有大量冗余数据的缺点,提出一种基于压缩感知(CS)理论的激光照明成像方法。阐述了压缩感知的基本原理,进行了图像恢复算法仿真。仿真结果表明:随着采样率的提高,成像质量有明显的提高;随着目标物体稀疏性的提高,图像重构需要的采样次数减少。设计了成像实验系统,实现了32像素×32像素的图像恢复,证明了所提成像技术的可行性。
Aiming at defect of a large amount of redundant data of the traditional laser imaging technology,a new kind of laser illuminated imaging method based on compressed sensing( CS) is proposed. The basic principle of CS is elaborated. Simulation of image restoration algorithm is carried out. Simulation result demonstrates that with the increase of sampling rate,the quality of imaging increases obviously; with the increase of sparsity of the target object,number of required samplings for image reconstruction decrease. Imaging experimental system is designed,image recovery of 32 pixel × 32 pixel is realized. Feasibility of this imageing technology is confirmed.
Aiming at defect of a large amount of redundant data of the traditional laser imaging technology,a new kind of laser illuminated imaging method based on compressed sensing( CS) is proposed. The basic principle of CS is elaborated. Simulation of image restoration algorithm is carried out. Simulation result demonstrates that with the increase of sampling rate,the quality of imaging increases obviously; with the increase of sparsity of the target object,number of required samplings for image reconstruction decrease. Imaging experimental system is designed,image recovery of 32 pixel × 32 pixel is realized. Feasibility of this imageing technology is confirmed.
引文
[1]Donoho D.Compressed sensing[J].IEEE Trans on Information Theory,2006,52(4):129-1306.
[2]Candes E,Romberg J,Tao T.Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information[J].IEEE Trans on Information Theory,2006,52(2):489-509.
[3]Candes E.Compressive sampling[C]∥Proceedings of International Congress of Mathematicians,2006:143-1452.
[4]Thkhar D,Laska J N,Wakin M B,et al.A new compressive imaging camera architecture using optical-domain compression[C]∥SPIE,2006:606509.
[5]Chen S,Donoho D,Saunders M.Atomic decomposition by basis pursuit[J].SIAM J Scientic Computing,1998,20(1):33-61.
[6]Kim S,Koh K,Lustig M,et al.An interior point method for largescale l1 regularized least squares[J].IEEE J Sel Top Signal Processing,2007,1(4):606-617.
[7]Fiqueiredo M,Nowak R,Wright S.Gradient projection for sparse reconstruction:Application to compressed sensing and other inverse problems[J].IEEE J Sel Top Signal Processing,2007,1(4):586-597.
[8]De Verse R A,Coifman R R,Coppi A C,et al.Application of spatial light modulators for new modalities in spectrometry and imaging[C]∥SPIE,2003:12-22.
[9]樊桂花,张廷华.脉冲激光回波信号采集技术[J].科技信息,2013(16):135-135.
[10]任彦林.基于FMC的数据采集及PXIe载板研制[D].哈尔滨:哈尔滨工业大学,2015.
[11]Zhang H,Hager W W.A nonmonotone line search technique and its application to unconstrained optimization[J].SIAM J Optim,2004,14:1043-1056.
[2]Candes E,Romberg J,Tao T.Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information[J].IEEE Trans on Information Theory,2006,52(2):489-509.
[3]Candes E.Compressive sampling[C]∥Proceedings of International Congress of Mathematicians,2006:143-1452.
[4]Thkhar D,Laska J N,Wakin M B,et al.A new compressive imaging camera architecture using optical-domain compression[C]∥SPIE,2006:606509.
[5]Chen S,Donoho D,Saunders M.Atomic decomposition by basis pursuit[J].SIAM J Scientic Computing,1998,20(1):33-61.
[6]Kim S,Koh K,Lustig M,et al.An interior point method for largescale l1 regularized least squares[J].IEEE J Sel Top Signal Processing,2007,1(4):606-617.
[7]Fiqueiredo M,Nowak R,Wright S.Gradient projection for sparse reconstruction:Application to compressed sensing and other inverse problems[J].IEEE J Sel Top Signal Processing,2007,1(4):586-597.
[8]De Verse R A,Coifman R R,Coppi A C,et al.Application of spatial light modulators for new modalities in spectrometry and imaging[C]∥SPIE,2003:12-22.
[9]樊桂花,张廷华.脉冲激光回波信号采集技术[J].科技信息,2013(16):135-135.
[10]任彦林.基于FMC的数据采集及PXIe载板研制[D].哈尔滨:哈尔滨工业大学,2015.
[11]Zhang H,Hager W W.A nonmonotone line search technique and its application to unconstrained optimization[J].SIAM J Optim,2004,14:1043-1056.