基于压缩感知成像系统的动态背景去噪

为了消除背景光强动态变化引起的非线性误差,提出了基于压缩感知成像系统的动态背景去噪算法.算法将测量中不同背景光强下所得测量值的平均值之差作为补偿系数,通过补偿系数消除动态背景噪声对压缩感知成像系统的非线性影响.仿真结果表明, 在总数为900次的采样过程中, 背影噪声动态变化300次时,算法能够将重建图像的峰值信噪比由29.5 dB提高到62 dB；在动态背景噪声的影响下, 本文算法能够大大提高目标图像的可读性, 提高成像质量, 增加压缩感知成像系统的鲁棒性.

Abstract

A algorithm which uses the compensation factor to eliminate nonlinear errors was proposed to reduce the nonlinear errors caused by sudden change of background in the imaging system based on compressed sensing. The compensation factor was calculated from the mean value of measured data under different background. The simulation results show that the proposed algorithm improve the peak signal to noise ratio of reconstructed image from 29.5 dB to 62 dB when the background noise change as many as 300 times during 900 times sampling. The reconstructed image is more readable and the imaging system is more robust with the proposed algorithm.

参考文献

[1] 徐健,常志国. 基于聚类的自适应图像稀疏表示算法及其应用［J］. 光子学报, 2011, 40(2): 316-320.

XU Jian, CHANG Zhi-guo. Self-adaptive image sparse representation algorithm based on clustering and its application［J］. Acta Photonica Sinica, 2011,40(2):316-320.

[2] HILLMAN R T, YAMAUCHI T, CHOI W, et al. Digital optical phase conjugation for delivering two-dimensional images through turbid media［J］,2013, Scitific Reports, 3:1909.

[3] CHUMNG K, WALLACE J, KIM S Y, et al. Structural and molecular interrogation of intact biological systems［J］. Nature,2013, 497:332-337.

[4] 刘海英, 李云松, 吴成柯. 一种数字微镜阵列分区控制和超分辨重建的压缩感知成像法［J］. 光子学报, 2014, 43(5): 0510002.

LIU Hai-ying, LI Yun-song, WU Cheng-ke. A method for compressive sensing of images based on zone control of digital micromirror device and super-resolution［J］. Acta Photonica Sinica, 2014, 43(5): 0510002

[5] CONKEY D B, CARAVACA-AGUIRRE A M. High-speed scattering medium characterization with application to focusing light through turbid media［J］. Optical Express,2012, 20:1733-1740.

[6] CANDES E J, WAKIN M B. An Introduction to compressive sampling［C］. IEEE Signal Process Magazine, 2008, 25:21-30.

[7] 卢佩, 刘效勇, 卢熙, 等. 基于压缩感知及光学理论的图像信息加密［J］. 光子学报, 2014, 43(9): 0910002.

LU Pei, LIU Xiao-yong, LU Xi, et al. Image information encryption by compressed sensing and optical theory［J］. Acta Photonica Sinica, 2014, 43(9): 0910002.

[8] DUARTE M F. Single-pixel imaging via compressive sampling［C］.IEEE Signal Process,2008, 25(2):83-91.

[9] RODRIGUEZ A D. Resolution analysis in computational imaging with patterned illumination and bucket detection［J］. Optical Letters,2014,39(13):3888-3891.

[10] ENRIQUE T. Image transmission through dynamic scattering media by single-pixel photodetection［J］. Optical Express, 2014, 22(14):16945-16955.

[11] YING Jin-pin, LIU Feng, ALFANO, et al. Effect of scattering on nonlinear optical scanning microscopy imaging of highly scattering media［J］.Applied Optical,2000, 39(4):509-514.

[12] ZIMNVAKOV, DMITRY A, ISAEV A, et al. Analysis of the scatter growth in dispersive media with the use of dynamic light scattering［J］. Applied Optical,2012,51(10):C62-C69.

[13] ZHUANG Jia-yan, CHEN Qian, HE Wei-ji, et al. Novel algorithm to compensate nonlinear response of photo detector to improve quality of image reconstruction for compressed sensing［J］.Optical Engineering,2013, 52(4):0432041.

[14] CANDES E, ROMBERG J, TAO T. Stable signal recovery from incomplete and inaccurate measurements［J］. Communications on Pure and Applied Mathematics,2006, 59(8):1207-1223.

[15] CANDES E, ROMBERG J, TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information［J］. IEEE Transactions on Infomation Theory,2006, 52(2):489-509.

[16] Software: l1-magic, http://www.dsp.ece.rice.edu/cs.

庄佳衍, 何伟基, 陈钱, 冯维一. 基于压缩感知成像系统的动态背景去噪[J]. 光子学报, 2015, 44(12): 1211003. ZHUANG Jia-yan, HE Wei-ji, CHEN Qian, FENG Wei-yi. Reducing the Error Caused by Dynamic Background in the Imaging System Based on Compressed Sensing[J]. ACTA PHOTONICA SINICA, 2015, 44(12): 1211003.

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