采用变分法的遥感影像条带噪声去除
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摘要
为了避免去除遥感影像条带噪声时丢失影像细节,构建了综合两种单向变分模型的组合去噪方法。首先,构建一种条带去除单向变分模型,通过此模型可以有效去除遥感影像条带噪声,从而初步获得近似遥感影像及条带噪声影像,同时避免近似遥感影像出现阶梯效应;其次,为了避免条带噪声影像中包含的少量细节信息丢失,构建一种条带保留单向变分模型,通过此模型可以去除条带噪声影像中的细节信息而只保留条带噪声,从而有效分离出细节信息;最后,把分离出的细节信息与近似遥感影像进行累加得到去噪影像。通过实验分析发现,本文方法不仅能完全去除条带噪声,而且基本没有破坏影像细节,去噪后的影像质量得到明显提高。
To avoid the loss of image details when removing the stripe noise of remote images, a combined destriping method of two unidirectional variation models was constructed. First, a stripe removal unidirectional variation model which can effectively remove the stripe noise and obtain the approximate restoration images and stripe noise images was constructed, and the stair effect is avoided in approximate remote images. Second, in order to avoid losing details in the stripe noise image, a stripe preserve unidirectional variation model was constructed, which could remove the image details from the stripe noise image and only preserve the stripe noise, so that the stripe noise and the image details could be separated effectively. Finally, the approximate remote image and the image details were superimposed to obtain the destriped image. Experimental results show that the proposed method can effectively restrain the stripe noise of remote images, and preserve the details of the remote images very well, hence the quality of destriped images is improved obviously.
To avoid the loss of image details when removing the stripe noise of remote images, a combined destriping method of two unidirectional variation models was constructed. First, a stripe removal unidirectional variation model which can effectively remove the stripe noise and obtain the approximate restoration images and stripe noise images was constructed, and the stair effect is avoided in approximate remote images. Second, in order to avoid losing details in the stripe noise image, a stripe preserve unidirectional variation model was constructed, which could remove the image details from the stripe noise image and only preserve the stripe noise, so that the stripe noise and the image details could be separated effectively. Finally, the approximate remote image and the image details were superimposed to obtain the destriped image. Experimental results show that the proposed method can effectively restrain the stripe noise of remote images, and preserve the details of the remote images very well, hence the quality of destriped images is improved obviously.
引文
[1] 孙斌, 李景林, 张星祥, 等. 600 mm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. SUN Bin, LI Jinglin, ZHANG Xingxiang, et al. Interleaving assemble of TDICCDs on 600 mm focal plane [J]. Optics and Precision Engineering, 2014, 22(11): 2908-2913.
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[10] BOUALI M, LADJAL S. Toward optimal destriping of MODIS data using a unidirectional variational model [J]. IEEE Transactions on Geoscience & Remote Sensing, 2011, 49(6): 2924-2935.
[11] CHANG Y, FANG H, YAN L, et al. Robust destriping method with unidirectional total variation and framelet regularization [J]. Opt Express, 2013, 21(7): 23307-23323.
[12] ZHANG Y, ZHOU G, YAN L, et al. A destriping algorithm based on TV-stokes and unidirectional total variation model [J]. Optik, 2016, 133: 428-439.
[13] LU X, WANG Y, YUAN Y. Graph-regularized low-rank representation for destriping of hyperspectral images [J]. IEEE Transactions on Geoscience & Remote Sensing, 2013, 51: 4009-4018.
[14] ZHOU G, FANG H, YAN L, et al. Removal of stripe noise with spatially adaptive unidirectional total variation [J]. Optik, 2014, 125(12): 3356-3362.
[15] 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. HUO Lijun, HE Bin, ZHOU Dabiao, et al. A destriping method with multi-scale variational model for remote sensing image [J]. Optics and Precision Engineering, 2017, 25(1): 198-206.
[16] BOUTEMEDJET A, DEND C, ZHAO B. Edge-aware unidirectional total variation model for stripe non-uniformity correction [J]. Sensors, 2018, 18(4): 1164.
[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. LI Xiaojie, REN Jianwei, LI Xiansheng, et al. Non-uniformity calibration and correction of reflection-based mosaic CCD camera [J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(6): 1057-1064.
[3] JINS C, YUN S, HUA D G, et al. Destriping MODIS data by power filtering [J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(9): 2119-2124.
[4] TORRES J, INFANTE S. Wavelet analysis for the elimination of striping noise in satellite images [J]. Optical Engineering, 2001, 40(7): 1309-1314.
[5] MUNCH B, TRTIK P, MARONE F, et al. Stripe and ring artifact removal with combined wavelet-Fourier filtering [J]. Opt Express, 2009, 17(5): 8567-8591.
[6] MICHAEL W. Destriping multiple sensor imagery by improved histogram matching [J]. International Journal of Remote Sensing, 2007, 11(5): 859-875.
[7] GADALLAH F, CSILLAG F, SMITH E. Destriping multisensory imagery with moment matching [J]. International Journal of Remote Sensing, 2000, 21(12): 2505-2511.
[8] 胡宝鹏, 周则明, 孟勇, 等. 基于矩匹配和变分法的条带去除模型 [J]. 红外, 2014, 35(11): 32-35. HU Baopeng, ZHOU Zeming, MENG Yong, et al. Destriping model of MODIS images based on moment matching and variational approach [J]. Infrared, 2014, 35(11): 35-35.
[9] 韩玲, 董连凤, 张敏, 等. 基于改进的矩匹配法高光谱影像条带噪声滤波技术 [J]. 光学学报, 2009, 29(12): 3333-3338. HAN Ling, DONG Lianfeng, ZHANG Min, et al. Destriping hyperspectral image based on an improved moment matching method [J]. Acta Optica Sinica, 2009, 29(12): 3333-3338.
[10] BOUALI M, LADJAL S. Toward optimal destriping of MODIS data using a unidirectional variational model [J]. IEEE Transactions on Geoscience & Remote Sensing, 2011, 49(6): 2924-2935.
[11] CHANG Y, FANG H, YAN L, et al. Robust destriping method with unidirectional total variation and framelet regularization [J]. Opt Express, 2013, 21(7): 23307-23323.
[12] ZHANG Y, ZHOU G, YAN L, et al. A destriping algorithm based on TV-stokes and unidirectional total variation model [J]. Optik, 2016, 133: 428-439.
[13] LU X, WANG Y, YUAN Y. Graph-regularized low-rank representation for destriping of hyperspectral images [J]. IEEE Transactions on Geoscience & Remote Sensing, 2013, 51: 4009-4018.
[14] ZHOU G, FANG H, YAN L, et al. Removal of stripe noise with spatially adaptive unidirectional total variation [J]. Optik, 2014, 125(12): 3356-3362.
[15] 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. HUO Lijun, HE Bin, ZHOU Dabiao, et al. A destriping method with multi-scale variational model for remote sensing image [J]. Optics and Precision Engineering, 2017, 25(1): 198-206.
[16] BOUTEMEDJET A, DEND C, ZHAO B. Edge-aware unidirectional total variation model for stripe non-uniformity correction [J]. Sensors, 2018, 18(4): 1164.