结合压缩感知和曲波的天文图像去噪
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摘要
在天文图像去噪中,为了提高迭代曲波阈值算法的去噪重建性能,提出了基于循环平移和曲波维纳滤波的压缩感知迭代重构算法。首先,使用基于曲波阈值的循环平移方法对重构图像进行调整以抑制重构图像中的伪吉布斯效应;接着,用提出的曲波维纳滤波算子替代小波阈值在迭代过程中对图像曲波系数进行筛选以进一步提高重构图像的质量。通过对添加高斯白噪声的Lena图像和月球图像进行重构实验,分析本文算法和当前主流算法的性能。实验结果表明,与传统的压缩感知迭代曲波阈值算法相比,本文算法能够获得较优的去噪性能,有效地保护天文图像的细节信息,峰值信噪比大约提高了2.6~3.2dB。
In astronomical image denoising,to improve denoising construction performance of iterative curvelet threshold(ICT)algorithm,a compressed sensing iterative reconstruction algorithm by combining cycle spinning and curvelet wiener filtering was proposed.Firstly,cycle spinning method based on curvelet threshold was used to adjust reconstructed images for inhibiting Pseudo-gibbs effect of reconstructed images;then,proposed curvelet wiener filtering operators were used to replace wavelet threshold for sieving image curvelet coefficient to further improve the quality of reconstructed image.The reconstruction experiment on Lena image and moon image with Gaussian white noise was conducted,and the result shows that compared with traditional compressed sensing ICT algorithm,the peak signal noise ratio of proposed algorithm increases by 2.6~3.2dB approximately.So the proposed method can acquire better denoising performance,and can protect detail information of astronomical images effectively.
In astronomical image denoising,to improve denoising construction performance of iterative curvelet threshold(ICT)algorithm,a compressed sensing iterative reconstruction algorithm by combining cycle spinning and curvelet wiener filtering was proposed.Firstly,cycle spinning method based on curvelet threshold was used to adjust reconstructed images for inhibiting Pseudo-gibbs effect of reconstructed images;then,proposed curvelet wiener filtering operators were used to replace wavelet threshold for sieving image curvelet coefficient to further improve the quality of reconstructed image.The reconstruction experiment on Lena image and moon image with Gaussian white noise was conducted,and the result shows that compared with traditional compressed sensing ICT algorithm,the peak signal noise ratio of proposed algorithm increases by 2.6~3.2dB approximately.So the proposed method can acquire better denoising performance,and can protect detail information of astronomical images effectively.
引文
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[19]SWAMY P M S,VANI K.A novel thresholding technique in the curvelet domain for improved speckle removal in sar images[J].Optik,2016,127(2):634-637.
[20]张晶晶,方勇华.基于Contourlet变换的遥感图像去噪新算法[J].光学学报,2008,28(3):462-466.ZHANG J J,FANG Y H.Novel denoising method for remote sensing image based on Contourlet transform[J].Acta Optica Sinca,2008,28(3):462-466.(in Chinese)
[21]郭海涛,赵红叶,徐雷,等.基于循环平移和DTCWT的声呐图像滤波方法[J].仪器仪表学报,2015,36(6):1350-1355.GUO H T,ZHAO H Y,XU L,et al..Sonar image filtering method based on cycle shift and DTCWT[J].Chinese Journal of Scientific Instrument,2015,36(6):1350-1355.(in Chinese)
[22]CANDES J,ROMBERG J,TAO T.Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theory,2006,52(2):489-509.
[23]STARCK J L,CANDES E J,DONOHO D L.The curvelet transform for image denoising[J].IEEE Transactions on Image Processing,2002,11(6):670-685.
[24]CANDES E,DEMANET L,DONOHO D.Fast discrete curvelet transforms[J].Multiscale Model.&Simul,2006,5(3):861-899.
[25]MIHCAK M K,KOZINTSEV I,RAMCHANDRAN K.Low-complexity image denoising based on statistical modeling of wavelet coefficients[J].IEEE Signal Processing Letters,1999,6(12):300-303.
[26]MA J W,Le DIMET F X.Deblurring from highly incomplete measurements for remote sensing[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(3):792-802.
[2]王忠良,冯燕,肖华,等.高光谱图像的分布式压缩感知成像与重构[J].光学精密工程,2015,23(4):1131-1137.WANG ZH L,FENG Y,XIAO H,et al..Distributed compressive sensing imaging and reconstruction of hyperspectral imagery[J].Opt.Precision Eng.,2015,23(4):1131-1137.(in Chinese)
[3]DONOHO D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[4]朱秋平,颜佳,张虎,等.基于压缩感知的多特征实时跟踪[J].光学精密工程,2013,21(2):437-444.ZHU Q P,YAN J,ZHANG H,et al..Real-time tracking using multiple features based on compressive sensing[J].Opt.Precision Eng.,2013,21(2):437-444.(in Chinese)
[5]胡辽林,王斌,薛瑞洋.压缩感知后引入噪声的信号恢复[J].光学精密工程,2014,22(10):2840-2846.HU L l,WANG B,XUE R Y.Signal recovery of noise introduced after compressed sensing[J].Opt.Precision Eng.,2014,22(10):2840-2846.(in Chinese)
[6]陈贞,邢笑雪.基于非下采样Shearlet变换与压缩感知的图像融合[J].液晶与显示,2015,30(6):1024-1031.CHEN ZH,XING X X.Image funsion algorithm based on non-subsampled Shearlet transform and compressed sensing[J].Chinese Journal of Liquid Crystals and Displays,2015,30(6):1024-1031.(in chinese)
[7]MA J W,PLONKA G.The curvelet transform[J].IEEE Signal Processing,2010,27(2):118-133.
[8]陈莹,朱明,刘剑,等.高斯混合模型自适应微光图像增强[J].液晶与显示,2015,30(2):300-309.CHEN Y,ZHU M,LIU J,et al..Automatic low light level image enhancement using Gaussian mixture modeling[J].Chinese Journal of Liquid Crystals and Displays,2015,30(2):300-309.(in chinese)
[9]杨新锋,张金娜,腾书华.利用压缩感知实现随机变频雷达散射中心估计[J].红外与激光工程,2016,45(5):0526004.YANG X F,ZHANG J N,TENG SH H.Parametric estimation of scattering center for random stepped frequency based on compressed sensing[J].Infrared and Laster Engineering,2016,45(5):0526004.(in chinese)
[10]王朋,荣志斌,何俊华,等.基于压缩感知的偏振光成像技术研究[J].红外与激光工程,2016,45(2):0228005:1-6.WANG P,RONG ZH B,HE J H,et al..Polarization imaging based on compressed sensing theory[J].Infrared and Laster Engineering,2016,45(2):0228005:1-6.(in chinese)
[11]BECK A,TEBOULLE M.A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J].SIAM J.Imaging Sci,2009,2(1):183-202.
[12]BLUMENSATH T,DAVIES M E.Iterative hard thresholding for compressed sensing[J].Applied and Computational Harmonic Analysis,2009,27(3):265-274.
[13]BARON F,MONNIER J D.Chara/imrc observations of two M supergiants in Perseus OB1:temperature,Bayesian modeling,and compressed sensing imaging[J].The Astrophysical Journal,2014,785(1):1-11.
[14]DASSIOS I,FOUNTOULAKIS K,GONDZIO J.A preconditioner for a primal-dual newton conjugate gradient method for compressed sensing problems[J].SIAM J.Sci.comput,2015,37(6):A2783-A2812.
[15]LIU E T,TEMLYAKOV V N.The orthogonal super greedy algorithm and applications in compressed sensing[J].IEEE Transactions on Information Theory,58(4):2040-2047.
[16]DO M N,VETTERLI M.The contourlet transform:an efficient directional multiresolution image representation[J].IEEE Transaction on Image Processing,2005,14(12):2091-2106.
[17]MA J W.Compressed sensing by inverse scale space and curvelet thresholding[J].Applied Mathematics and Computation,2008,206(2):980-988.
[18]ESLAHI N,AGHAGOLZAOEH A.Compressive sensing image restoration using adaptive curvelet thresholding and nonlocal sparse regularization[J].IEEE Transactions on Image Processing,2016,25(7):3216-3140.
[19]SWAMY P M S,VANI K.A novel thresholding technique in the curvelet domain for improved speckle removal in sar images[J].Optik,2016,127(2):634-637.
[20]张晶晶,方勇华.基于Contourlet变换的遥感图像去噪新算法[J].光学学报,2008,28(3):462-466.ZHANG J J,FANG Y H.Novel denoising method for remote sensing image based on Contourlet transform[J].Acta Optica Sinca,2008,28(3):462-466.(in Chinese)
[21]郭海涛,赵红叶,徐雷,等.基于循环平移和DTCWT的声呐图像滤波方法[J].仪器仪表学报,2015,36(6):1350-1355.GUO H T,ZHAO H Y,XU L,et al..Sonar image filtering method based on cycle shift and DTCWT[J].Chinese Journal of Scientific Instrument,2015,36(6):1350-1355.(in Chinese)
[22]CANDES J,ROMBERG J,TAO T.Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theory,2006,52(2):489-509.
[23]STARCK J L,CANDES E J,DONOHO D L.The curvelet transform for image denoising[J].IEEE Transactions on Image Processing,2002,11(6):670-685.
[24]CANDES E,DEMANET L,DONOHO D.Fast discrete curvelet transforms[J].Multiscale Model.&Simul,2006,5(3):861-899.
[25]MIHCAK M K,KOZINTSEV I,RAMCHANDRAN K.Low-complexity image denoising based on statistical modeling of wavelet coefficients[J].IEEE Signal Processing Letters,1999,6(12):300-303.
[26]MA J W,Le DIMET F X.Deblurring from highly incomplete measurements for remote sensing[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(3):792-802.