基于压缩感知的反射光谱重构算法研究

doi:10.3964/j.issn.1000-0593(2021)04-1092-05

Abstract
References
Related Citation (15)

Download: PDF (1098 KB)
Export: BibTeX | EndNote (RIS)

Abstract The spectral reflectance describes the surface color characteristics of the object. In order to obtain more accurate color information of the object, the spectral reflectance reconstruction in the image processing field has become a hot topic. We take the spectral reflectance as the main research target. Sequentially, we propose the algorithm which reconstructs the spectral reflectance of the measured object in the visible region to enhance the accuracy of color reproduction. This article attempts to employ compressed sensing (CS) theory in the spectral experiments to reconstruct the spectral reflectance. This article first introduces the theory of compressive sensing and then combines the theory of compressive sensing with the principle of spectral reflectance. According to the theoretical framework of spectral reflectance reconstruction based on compressed sensing. The appropriate sampling value is selected.The compressed sensing sample value is the compressed value, the wavelet base is used as the orthogonal matrix, and the Gaussian random matrix is used as the measurement matrix, the orthogonal matrix and the measurement matrix ensure irrelevance, the original spectral reflectance is linearly projected from the high dimension to the low dimension, then a low-dimensional observation signal is obtained, the simple orthogonal matching pursuit algorithm (OMP) reconstructs the low-dimensional to high-dimensional high-precision observation signals from low-dimensional observation signals, and the obtained spectral reflectance has the same dimensions as the original spectral reflectance. Finally, the compressed inverse method and the traditional spectral reflectance reconstruction algorithm are compared with thepseudo-inverse method and the polynomial regression method. The experimental results showthat the color difference and the root mean square error obtained by the compressed sensing reconstruction algorithm are smaller than the measured value of the pseudo-inverse method and the polynomial regression method, In other words, the reconstruction accuracy is significantly improved, the spectral curve reconstructed by compressed sensing can reach or be closer to the peak of the original spectral curve, which is closer to the original spectral curve on the whole visible range. The spectral curve reconstructed by the polynomial regression method and the pseudo-inverse method does not reach the original peak, and there is an overall deviation. Inconclusion, the experimental resultsshow that compressed sensing uses low-sampling data to achieve the effect of full sampling. Compressed sensing improves the accuracy of spectral reflectance reconstruction while reducing the amount of computation. The compression reconstruction effect proposed in this paper is significantly better than the traditional polynomial regression method and the pseudo-inverse method. Compressed sensing theory can be applied to practical multispectral imaging systems.

Key words：Compressed sensing; Spectral reflectance; Spectral reconstruction; Multispectral imaging

Received: 2020-03-31 Accepted: 2020-08-16

ZTFLH:

O433.4

Cite this article:

URL:

http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)04-1092-05 OR http://www.gpxygpfx.com/EN/Y2021/V41/I04/1092

[1] ZHAO Shou-bo, QU Xing-hua, FENG Wei, et al(赵首博, 曲兴华, 冯维, 等). Optoelectronic Engineering(光电工程), 2016，43(1): 13.
[2] LI Chan, WAN Xiao-xia, LIANG Jin-xing(李婵，万晓霞，梁金星). Journal of Luminescence(发光学报)，2016，37(12)：1571.
[3] XIAO Ying, MA Xiang-cai, WANG Dong-dong, et al(肖颖，麻祥才，王东东，等). Optical Technology(光学技术)，2019，45(6)：696.
[4] LIU Chuan-jie, LI Yu-mei, CHEN Hao-jie, et al(刘传杰，李玉梅，陈浩杰，等). Pcakaging Engineering(包装工程)，2018，39(1)：168.
[5] WANG Yu-ye, REN Yu-chen, CHEN Lin-yu，et al(王与烨, 任宇琛, 陈霖宇, 等). Acta Optica Sinica(光学学报)，2019，(4): 108.
[6] WANG Tian-yun, LIU Bing, WEI Qiang(王天云, 刘冰, 魏强). Electron Optics & Control(电光与控制)，2019, 26(7): 1.
[7] KE Jun, ZHANG Lin-xia, ZHOU Qun(柯钧，张临夏，周群). Acta Optica Sinica(光学学报)，2020，40(1)：98.
[8] Wang L, Feng Y, Gao Y, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(4): 1266.
[9] FAN Jian-ying, MA Ming-yang, ZHAO Shou-bo(范建英, 马明阳, 赵首博). Journal of Electronics and Information Technology(电子与信息学报), 2020, 42(4): 1013.
[10] Escorcia I, Grant J, Gough J, et al. Optics Letters, 2016, 41(14): 3261.
[11] YANG Ying, KONG Ling-jun, LIU Zhen(杨鹰，孔玲君，刘真). Liquid Crystal and Disply(液晶与显示)，2017，32(1)：56.
[12] WANG Qi, MA Ling-ling, TANG Ling-li, et al(汪琪，马灵玲，唐伶俐，等). Journal of Infrared and Millimeter Waves(红外与毫米波学报)，2016，35(6)：723.
[13] Iliadis M, Spinoulas L, Kaysaggelos A K. Optical Engineering，2017, 56(8): 84106.
[14] ZHANG Shu-fang, ZHU Bin-hua, LI Rui(张淑芳, 朱彬华, 李瑞). Progress in Laser and Optoelectronics(激光与光电子学进展)，2017，54(11): 164.