Landsat影像垂直方向条带噪声去除方法研究
|
摘要
针对条带噪声去除过程中容易丢失细节信息、去噪后的影像出现模糊及阶梯效应等问题,提出两种变分模型去除条带噪声.首先,构建一种初步条带噪声分离模型(PSDM),通过交替方向乘子方法同时解求出恢复影像及条带噪声影像;其次,构建一种条带噪声变分模型(SNVM),此模型可以达到去除条带噪声影像中的细节信息而只保留条带噪声的目的;最后,把恢复影像与条带噪声影像中的细节信息进行累加获得去噪影像.实验分析结果表明:该方法能保证在影像扭曲度很小的情况下有效去除条带噪声,并且在影像细节保留及去噪后影像质量提升两方面都是最优的.
According to some problems in the process of destriping,such as destriping image blurred,the large loss of image details,generating step effect in image stationary region and so on,two variation models were constructed.First,a preliminary stripe noise decomposition model was constructed,and the restoration image and the stripe noise image were solved by the alternating direction multipliers method.Then,in order to avoid losing details contained in the stripe noise image,a stripe noise variation model was constructed.This model could effectively remove the details from the stripe noise image and only preserve the strip noise,so that the stripe noise and the details could be separated effectively.Finally,the restoration image and the details were added to obtain the destriping image.Experimental results show that the proposed method can effectively remove the stripe noise under small distortion for remote sensing images without destroying the image details basically,and the quality of destriping image is improved obviously.
According to some problems in the process of destriping,such as destriping image blurred,the large loss of image details,generating step effect in image stationary region and so on,two variation models were constructed.First,a preliminary stripe noise decomposition model was constructed,and the restoration image and the stripe noise image were solved by the alternating direction multipliers method.Then,in order to avoid losing details contained in the stripe noise image,a stripe noise variation model was constructed.This model could effectively remove the details from the stripe noise image and only preserve the strip noise,so that the stripe noise and the details could be separated effectively.Finally,the restoration image and the details were added to obtain the destriping image.Experimental results show that the proposed method can effectively remove the stripe noise under small distortion for remote sensing images without destroying the image details basically,and the quality of destriping image is improved obviously.
引文
[1]孙斌,李景林,张星祥,等.600mm长焦面时间延迟积分CCD的交错拼接[J].光学精密工程,2014,22(11):2908-2913.
[2]李晓杰,任建伟,李宪圣,等.反射式拼接CCD相机非均匀性定标与校正[J].液晶与显示,2014,29(6):1057-1064.
[3]PANDE-CHHETRI R,ABD-ELRAHMAN A.De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering[J].ISPRS Journal ofPhotogrammetry and Remote Sensing.2011,66(5):620-636.
[4]张霞,孙伟超,帅通,等.基于小波变换的图像条带噪声去除方法[J].遥感技术与应用.2015,30(6):1168-1175.
[5]MUNCH B,TRTIk P,MARONE F,et al.Stripe and ring artifact removal with combined wavelet-Fourier filtering[J].Optics Express,2009,17(5):8567-8591.
[6]HAN L,DONG L F,ZHANG M,et al.Destriping hyperspectral image based on an improved moment matching method[J].Acta Optica Sinica,2009,29(12):3333-3338.
[7]SUN L,NEVILLE R,STAENZ K,et al.Automatic destriping of Hyperion imagery based on spectral moment matching[J].Canadian Journal of Remote Sensing,2014,34(S1):568-581.
[8]BOUALI M,LASJAL S.Toward optimal destriping of MODIS data using a unidirectional variational model[J].IEEE Transactions on Geoscience&Remote Sensing,2011,49(6),2924-2935.
[9]LIU X,LU X,SHEN H,et al.Stripe noise separation and removal in remote sensing images by consideration of the global sparsity and local variational Properties[J].IEEETransactions on Geoscience&Remote Sensing,2016,54(5):3049-3060.
[10]CHANG Y,YAN L,FANG H,et al.Anisotropic spectral-spatial total variation model for multispectral remote sensing image destriping[J].IEEE Transactions on Image Processing,2015,24(6):1852-1866.
[11]ZHOU G,FANG H,YAN L,et al.Removal of stripe noise with spatially adaptive unidirectional total variation[J].Optik,2014,125(12):2756-2762.
[12]霍丽君,何斌,周达标,等.遥感影像条带噪声的多尺度变分模型去除[J].光学精密工程,2017,25(1):198-206.
[13]ZHANG Y,ZhOU G,YAN L,et al.A destriping algorithm based on TV-Stokes and unidirectional total variation model[J].Optik,2016,127:428-439.
[14]LU X,WANG Y,YUAN Y.Graph-regularized low-rank representation for destriping of hyperspectral images[J].IEEE Transactions on Geoscience&Remote Sen-sing,2013,51:4009-4018.
[15]BOUTEMEDJETA,DENDC,ZHAO B.Edge-aware unidirectional total variation model for stripe non-uniformity correction[J].Sensors,2018,18(4):1164-1177.
[2]李晓杰,任建伟,李宪圣,等.反射式拼接CCD相机非均匀性定标与校正[J].液晶与显示,2014,29(6):1057-1064.
[3]PANDE-CHHETRI R,ABD-ELRAHMAN A.De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering[J].ISPRS Journal ofPhotogrammetry and Remote Sensing.2011,66(5):620-636.
[4]张霞,孙伟超,帅通,等.基于小波变换的图像条带噪声去除方法[J].遥感技术与应用.2015,30(6):1168-1175.
[5]MUNCH B,TRTIk P,MARONE F,et al.Stripe and ring artifact removal with combined wavelet-Fourier filtering[J].Optics Express,2009,17(5):8567-8591.
[6]HAN L,DONG L F,ZHANG M,et al.Destriping hyperspectral image based on an improved moment matching method[J].Acta Optica Sinica,2009,29(12):3333-3338.
[7]SUN L,NEVILLE R,STAENZ K,et al.Automatic destriping of Hyperion imagery based on spectral moment matching[J].Canadian Journal of Remote Sensing,2014,34(S1):568-581.
[8]BOUALI M,LASJAL S.Toward optimal destriping of MODIS data using a unidirectional variational model[J].IEEE Transactions on Geoscience&Remote Sensing,2011,49(6),2924-2935.
[9]LIU X,LU X,SHEN H,et al.Stripe noise separation and removal in remote sensing images by consideration of the global sparsity and local variational Properties[J].IEEETransactions on Geoscience&Remote Sensing,2016,54(5):3049-3060.
[10]CHANG Y,YAN L,FANG H,et al.Anisotropic spectral-spatial total variation model for multispectral remote sensing image destriping[J].IEEE Transactions on Image Processing,2015,24(6):1852-1866.
[11]ZHOU G,FANG H,YAN L,et al.Removal of stripe noise with spatially adaptive unidirectional total variation[J].Optik,2014,125(12):2756-2762.
[12]霍丽君,何斌,周达标,等.遥感影像条带噪声的多尺度变分模型去除[J].光学精密工程,2017,25(1):198-206.
[13]ZHANG Y,ZhOU G,YAN L,et al.A destriping algorithm based on TV-Stokes and unidirectional total variation model[J].Optik,2016,127:428-439.
[14]LU X,WANG Y,YUAN Y.Graph-regularized low-rank representation for destriping of hyperspectral images[J].IEEE Transactions on Geoscience&Remote Sen-sing,2013,51:4009-4018.
[15]BOUTEMEDJETA,DENDC,ZHAO B.Edge-aware unidirectional total variation model for stripe non-uniformity correction[J].Sensors,2018,18(4):1164-1177.