基于改进逆滤波的衍射成像光谱仪图像复原方法
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摘要
针对在衍射光谱仪(DOIS)成像中离焦谱段对准焦谱段成像造成干扰而导致的图像模糊问题,提出一种改进的逆滤波复原方法,旨在解决逆滤波中存在的不适定问题,并利用该方法对衍射光谱图像进行复原。改进的逆滤波算法通过引入正则化矩阵来改变原始问题的求解形式,将逆滤波函数进行正则化,从而减弱噪声对图像复原效果所产生的影响。通过将图像复原过程转换为矩阵求逆的过程,并在SVD算法求解过程中添加规则滤波器的方法,来调节正则化矩阵的形式以及参数的大小,达到了减弱矩阵的病态性并取得较优的复原效果的目的。实验结果表明,该方法能够有效地对衍射成像光谱仪图像进行复原,在一定程度上提高了拉普拉斯梯度以及图像质量指数(QI)值,同时减小了均方根(RMSE)值。所提方法能够抑制噪声干扰,增强图像清晰度,复原出与参考图相似度更高的单谱段图像,并能够获得更好的光谱曲线,有助于分析出地貌特征。
In order to solve the image-blurring problem caused by the interference from out-of-focus optical images in infocus image within a diffractive optic imaging spectrometer(DOIS),an improved inverse filtering restoration method was proposed to solve the ill-posed problem in inverse filtering and restore the diffraction spectrum image.This method changes the solution of the primal problem by introducing a regularization matrix to regularize the inverse filtering function,thus suppressing the influences of noises on restored images.It achieves the purpose of reducing morbidity of the matrix and obtaining a better restoration result through the following three procedures:convert the image restoration process into a process of matrix inversion,add a regular filter to the SVD(singular value decomposition)method,and adjust the form of the regularization matrix and the values of parameters.Experiments show that the improved inverse filtering method is effective for restoring the spectral images formed with a diffractive optic imaging spectrometer.It can not only increase the Laplacian Gradient and QI(Quality Index)value,but also reduce RMSE(Root-Mean-Square Error)value to a certain extent.In the meantime,this method can suppress the noise interferences of the blurred images,enhance the image clarity,restore a single spectrum image with a higher similarity to the reference image,and obtain better spectral curves to analyze the geomorphological features.
In order to solve the image-blurring problem caused by the interference from out-of-focus optical images in infocus image within a diffractive optic imaging spectrometer(DOIS),an improved inverse filtering restoration method was proposed to solve the ill-posed problem in inverse filtering and restore the diffraction spectrum image.This method changes the solution of the primal problem by introducing a regularization matrix to regularize the inverse filtering function,thus suppressing the influences of noises on restored images.It achieves the purpose of reducing morbidity of the matrix and obtaining a better restoration result through the following three procedures:convert the image restoration process into a process of matrix inversion,add a regular filter to the SVD(singular value decomposition)method,and adjust the form of the regularization matrix and the values of parameters.Experiments show that the improved inverse filtering method is effective for restoring the spectral images formed with a diffractive optic imaging spectrometer.It can not only increase the Laplacian Gradient and QI(Quality Index)value,but also reduce RMSE(Root-Mean-Square Error)value to a certain extent.In the meantime,this method can suppress the noise interferences of the blurred images,enhance the image clarity,restore a single spectrum image with a higher similarity to the reference image,and obtain better spectral curves to analyze the geomorphological features.
引文
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[3]LYONS D M.Image spectrometry with a diffractive optic[C]∥Proceedings of SPIE-The International Society for Optical Engineering.1995:123-132.
[4]HINNRICHS M.Imaging spectrometer for fugitive gas leak detection[C]∥Proceedings of SPIE-The International Society for Optical Engineering.1999:152-162.
[5]Gas Imaging Technology LLC.The Image Spectrometer Company specializing in infrared gas detection technology to help clean up the environment[EB/OL].(2000-2011)[2014/5/28].http://gitint.com.
[6]GUPTA N.LWIR hyperspectral imager based on a diffractive optics lens[J].Proc Spie,2009,7303(4):32.
[7]SUN Q,YU B,WANG Z Q,et al.Study on hyperspectral detection system with the harmonic diffractive element in infrared dual-band[J].Acta Physica Sinica,2004,53(3):756-761.(in Chinese)孙强,于斌,王肇圻,等.谐衍射双波段红外超光谱探测系统研究[J].物理学报,2004,53(3):756-761.
[8]WANG D,YAN Y,JIN G,et al.A Spectrometer Based on Diffractive Lens[J].Chinese Journal of Lasers,2001,10(4):246-250.
[9]LIANG J Y.Research on diffractive spectral imaging[D].Hangzhou:Zhejiang University,2012.(in Chinese)梁靖宇.衍射光谱成像研究[D].杭州:浙江大学,2012.
[10]WU X Y,WU J,ZHANG H.Research on image restoration techniques based on inverse filtering algorithm[J].Information Technology,2011(10):183-185.(in Chinese)吴雪垠,吴谨,张鹤.逆滤波法在图像复原中的应用[J].信息技术,2011(10):183-185.
[11]WU W,BI G.Image restoration in T-ray imaging based on wavelet de-noising[J].Infrared and Laser Engineering,2005,34(5):592.
[12]ESKICIOGLU A M,FISHER P S.Image quality measures and their performance[J].IEEE Trans Commun,1995,43(12):2959-2965.
[13]ZENG W M.Restoration algorithm and their experimental verification for diffractive optic imaging spectrometer[D].Suzhou:Suzhou University,2014.(in Chinese)曾文明.衍射透镜成像光谱仪复原算法与实验验证[D].苏州:苏州大学,2014.
[14]GOLUB G H,HANSEN P C,O’LEARY D P.Tikhonov regularization and total least squares[J].SIAM Journal on Matrix Analysis and Applications,1999,21(1):185-194.
[15]LIU H C.Research on image definition evaluation algorithm for video surveillance[D].Nanjing:Dongnan University,2017.刘瀚存.视频监控图像清晰度评价算法研究[D].南京:东南大学,2017.
[16]ZHOU Y W,YANG P L,CHEN Q,et al.Pan-sharpening Model Based on MTF and Variational Method[J].Acta Automatica Sinica,2015,41(2):342-352.(in Chinese)周雨薇,杨平吕,陈强,等.基于MTF和变分的全色与多光谱图像融合模型[J].自动化学报,2015,41(2):342-352.
[17]FENG M K,ZHAO S M,SUN L H,et al.Image quality assessment based on local Gaussian weithted fusion[J].Computer Engineering,2016,42(8):237-242.(in Chinese)丰明坤,赵生妹,孙丽慧,等.基于局部高斯加权融合的图像质量评价[J].计算机工程,2016,42(8):237-242.