遥感影像变分Mask自适应匀光算法
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摘要
针对Mask匀光法对低通滤波器及其尺寸参数依赖的问题,提出一种变分Mask自适应匀光算法。根据影像自身的先验信息,结合Mask数学模型构造变分能量函数,分别利用各向异性和各向同性全变分正则项约束理想影像和背景影像,采用分裂Bregman方法交替迭代求解,可同时获取最优化背景影像和理想影像。利用3幅光照不均匀的遥感影像进行实验并与3种不同滤波尺寸的Mask匀光法进行了比较,本文方法在目视效果和定量评价指标上均呈现明显的优势,在消除亮度不均的同时能够保持影像的纹理和细节信息。
The illumination of a captured remote sensing image may be unevenly distributed because of the influences of sensors and external factors, such as light and fog. Uneven illumination degrades the quality of an image and causes interference in the subsequent processing(e.g., image interpretation, image classification, and image mosaicking). The process of correcting uneven illumination is called image dodging, which is of great practical significance and application value. Current methods focus on how to obtain the illumination distribution within an image and compensate for the different parts of the image in different degrees. The mask dodging method is widely used for remote sensing images. The background is generated by a low-pass filter to simulate the illumination distribution and subtracted from the original image to obtain the ideal image with even illumination. Estimation of the background image is a key step in the mask dodging method and directly affects the resulting image, which depends on the lowpass filter and its dimension parameter. Automatic selection of the dimension parameter is a problem in the mask dodging method. However,no suitable solution to the problem exists, and manual adjustment is usually required in practical applications. An adaptive dodging method based on a variational mask is proposed in this study to resolve the poor universality of the mask dodging method. A variational energy function is constructed according to the prior information of the image itself and the mathematical model of the mask dodging method. L1 and L2 norms are adopted to constrain the texture and detail of the ideal image and the smoothness of the background image, respectively. Estimation of the background image is transformed into an optimal solution of the variational energy function.Solving the problem directly is often difficult because the variational function contains two norms. The split Bregman method is used to decompose the variational function into three subproblems, which are solved by alternate iteration, to simplify the calculation. Optimal background and ideal images are then obtained simultaneously. Two groups of experiments are conducted on three remote sensing images to verify the effectiveness of the proposed method. Compared with mask dodging methods with three different dimension parameters, the proposed method performs better in visual evaluation and quantitative measurements. The proposed method can effectively eliminate uneven illumination while maintaining the textures and details of the remote sensing images.
The illumination of a captured remote sensing image may be unevenly distributed because of the influences of sensors and external factors, such as light and fog. Uneven illumination degrades the quality of an image and causes interference in the subsequent processing(e.g., image interpretation, image classification, and image mosaicking). The process of correcting uneven illumination is called image dodging, which is of great practical significance and application value. Current methods focus on how to obtain the illumination distribution within an image and compensate for the different parts of the image in different degrees. The mask dodging method is widely used for remote sensing images. The background is generated by a low-pass filter to simulate the illumination distribution and subtracted from the original image to obtain the ideal image with even illumination. Estimation of the background image is a key step in the mask dodging method and directly affects the resulting image, which depends on the lowpass filter and its dimension parameter. Automatic selection of the dimension parameter is a problem in the mask dodging method. However,no suitable solution to the problem exists, and manual adjustment is usually required in practical applications. An adaptive dodging method based on a variational mask is proposed in this study to resolve the poor universality of the mask dodging method. A variational energy function is constructed according to the prior information of the image itself and the mathematical model of the mask dodging method. L1 and L2 norms are adopted to constrain the texture and detail of the ideal image and the smoothness of the background image, respectively. Estimation of the background image is transformed into an optimal solution of the variational energy function.Solving the problem directly is often difficult because the variational function contains two norms. The split Bregman method is used to decompose the variational function into three subproblems, which are solved by alternate iteration, to simplify the calculation. Optimal background and ideal images are then obtained simultaneously. Two groups of experiments are conducted on three remote sensing images to verify the effectiveness of the proposed method. Compared with mask dodging methods with three different dimension parameters, the proposed method performs better in visual evaluation and quantitative measurements. The proposed method can effectively eliminate uneven illumination while maintaining the textures and details of the remote sensing images.
引文
Chen C and Xu G L.2016.A new linearized split Bregman iterative algorithm for image reconstruction in sparse-view X-ray computed tomography.Computers and Mathematics with Applications,71(8):1537-1559[DOI:10.1016/j.camwa.2016.01.003]
Fan C N and Zhang F Y.2011.Homomorphic filtering based illumination normalization method for face recognition.Pattern Recognition Letters,32(10):1468-1479[DOI:10.1016/j.patrec.2011.03.023]
Guo C Z,Shi W J,Qin Z Y and Geng Z X.2016.Non-convex sparsity regularization for wave back restoration of space object images.Optics and Precision Engineering,24(4):902-912(郭从洲,时文俊,秦志远,耿则勋.2016.空间目标图像的非凸稀疏正则化波后复原.光学精密工程,24(4):902-912)[DOI:10.3788/OPE.20162404.0902]
Huo L J,He B and Zhou D B.2017.A destriping method with multiscale variational model for remote sensing image.Optics and Precision Engineering,25(1):198-207(霍丽君,何斌,周达标.2017.遥感图像条带噪声的多尺度变分模型去除.光学精密工程,25(1):198-207)[DOI:10.3788/OPE.20172501.0198]
Jiang B,Woodell G A and Jobson D J.2015.Novel multi-scale Retinex with color restoration on graphics processing unit.Journal of Real-Time Image Processing,10(2):239-253[DOI:10.1007/s11554-014-0399-9]
Kimmel R,Elad M,Shaked D,Keshet R and Sobel I.2003.A variational framework for retinex.International Journal of Computer Vision,52(1):7-23[DOI:10.1023/A:1022314423998]
Li D R,Wang M and Pan J.2006.Auto-dodging processing and its application for optical RS images.Geomatics and Information Science of Wuhan University,31(9):753-756(李德仁,王密,潘俊.2006.光学遥感影像的自动匀光处理及应用.武汉大学学报(信息科学版),31(9):753-756)[DOI:10.3321/j.issn.1671-8860.2006.09.001]
Li H F,Shen H F,Zhang L P and Li P X.2010.An uneven illumination correction method based on variational Retinex for remote sensing image.Acta Geodaetica et Cartographica Sinica,39(6):585-591(李慧芳,沈焕锋,张良培,李平湘.2010.一种基于变分Retinex的遥感影像不均匀性校正方法.测绘学报,39(6):585-591)
Sun M W.2009.Research on key technology of automatic and fast DOM generation.Wuhan:Wuhan University(孙明伟.2009.正射影像全自动快速制作关键技术研究.武汉:武汉大学)
Tang S,Gong W G and Zhong J H.2013.Sparsity and smoothing multi-regularization constraints for blind image deblurring.Journal of Software,24(5):1143-1154(唐述,龚卫国,仲建华.2013.稀疏平滑特性的多正则化约束图像盲复原方法.软件学报,24(5):1143-1154)[DOI:10.3724/SP.J.1001.2013.04256]
Wang M and Pan J.2004.A method of removing the uneven illumination for digital aerial image.Journal of Image and Graphics,9(6):744-748(王密,潘俊.2004.一种数字航空影像的匀光方法.中国图象图形学报,9(6):744-748)[DOI:10.3969/j.issn.1006-8961.2004.06.016]
Yao F,Wan Y C and Hu H.2013.Research on the improved image dodging algorithm based on Mask technique.Remote Sensing Information,28(3):8-13(姚芳,万幼川,胡晗.2013.基于Mask原理的改进匀光算法研究.遥感信息,28(3):8-13)[DOI:10.3969/j.issn.1000-3177.2013.03.002]
Yuan X X,Han Y T and Fang Y.2014.Improved Mask dodging algorithm for aerial imagery.Journal of Remote Sensing,18(3):630-641(袁修孝,韩宇韬,方毅.2014.改进的航摄影像Mask匀光算法.遥感学报,18(3):630-641)[DOI:10.11834/jrs.20143295]
Zhang Z,Zhu B S and Zhu S L.2010.An improved MASK dodging method based on contrast consistency processing.Journal of Geomatics Science and Technology,27(1):54-56(张振,朱宝山,朱述龙.2010.反差一致性改进的MASK匀光算法.测绘科学技术学报,27(1):54-56)[DOI:10.3969/j.issn.1673-6338.2010.01.014]
Zhang Z,Zhu B S,Zhu S L and Cao W.2009.Improved MASKdodging method based on wavelet.Journal of Remote Sensing,13(6):1074-1081(张振,朱宝山,朱述龙,曹闻.2009.小波变换改进的MASK匀光算法.遥感学报,13(6):1074-1081)[DOI:10.11834/jrs.20090607]
Zhang Z X,Li Z J,Zhang J Q and Zheng L.2003.Use discrete chromatic space to tune the image tone in a color image mosaic//Proceedings of the Third International Symposium on Multispectral Image Processing and Pattern Recognition.Beijing:SPIE[DOI:10.1117/12.538550]
Zhang Z X and Zou S B.2011.An improved algorithm of Mask image dodging for aerial image//Proceedings of MIPPR 2011:Remote Sensing Image Processing,Geographic Information Systems and Other Applications.Guilin:SPIE[DOI:10.1117/12.901869]
Zheng J,Xu C G,Xiao D G,Li H and Huang H.2003.The technique of digit image’s illumination uneven elimination.Transactions of Beijing Institute of Technology,23(3):285-289(郑军,徐春广,肖定国,理华,黄卉.2003.数字图像中照度不均匀校正技术研究.北京理工大学学报,23(3):285-289)[DOI:10.3969/j.issn.1001-0645.2003.03.006]
Zhu S L,Zhang Z,Zhu B S and Cao W.2011.Experimental comparison among five algorithms of brightness and contrast homogenization.Journal of Remote Sensing,15(1):111-122(朱述龙,张振,朱宝山,曹闻.2011.遥感影像亮度和反差分布不均匀性校正算法的效果比较.遥感学报,15(1):111-122)[DOI:10.11834/jrs.20110109]
Fan C N and Zhang F Y.2011.Homomorphic filtering based illumination normalization method for face recognition.Pattern Recognition Letters,32(10):1468-1479[DOI:10.1016/j.patrec.2011.03.023]
Guo C Z,Shi W J,Qin Z Y and Geng Z X.2016.Non-convex sparsity regularization for wave back restoration of space object images.Optics and Precision Engineering,24(4):902-912(郭从洲,时文俊,秦志远,耿则勋.2016.空间目标图像的非凸稀疏正则化波后复原.光学精密工程,24(4):902-912)[DOI:10.3788/OPE.20162404.0902]
Huo L J,He B and Zhou D B.2017.A destriping method with multiscale variational model for remote sensing image.Optics and Precision Engineering,25(1):198-207(霍丽君,何斌,周达标.2017.遥感图像条带噪声的多尺度变分模型去除.光学精密工程,25(1):198-207)[DOI:10.3788/OPE.20172501.0198]
Jiang B,Woodell G A and Jobson D J.2015.Novel multi-scale Retinex with color restoration on graphics processing unit.Journal of Real-Time Image Processing,10(2):239-253[DOI:10.1007/s11554-014-0399-9]
Kimmel R,Elad M,Shaked D,Keshet R and Sobel I.2003.A variational framework for retinex.International Journal of Computer Vision,52(1):7-23[DOI:10.1023/A:1022314423998]
Li D R,Wang M and Pan J.2006.Auto-dodging processing and its application for optical RS images.Geomatics and Information Science of Wuhan University,31(9):753-756(李德仁,王密,潘俊.2006.光学遥感影像的自动匀光处理及应用.武汉大学学报(信息科学版),31(9):753-756)[DOI:10.3321/j.issn.1671-8860.2006.09.001]
Li H F,Shen H F,Zhang L P and Li P X.2010.An uneven illumination correction method based on variational Retinex for remote sensing image.Acta Geodaetica et Cartographica Sinica,39(6):585-591(李慧芳,沈焕锋,张良培,李平湘.2010.一种基于变分Retinex的遥感影像不均匀性校正方法.测绘学报,39(6):585-591)
Sun M W.2009.Research on key technology of automatic and fast DOM generation.Wuhan:Wuhan University(孙明伟.2009.正射影像全自动快速制作关键技术研究.武汉:武汉大学)
Tang S,Gong W G and Zhong J H.2013.Sparsity and smoothing multi-regularization constraints for blind image deblurring.Journal of Software,24(5):1143-1154(唐述,龚卫国,仲建华.2013.稀疏平滑特性的多正则化约束图像盲复原方法.软件学报,24(5):1143-1154)[DOI:10.3724/SP.J.1001.2013.04256]
Wang M and Pan J.2004.A method of removing the uneven illumination for digital aerial image.Journal of Image and Graphics,9(6):744-748(王密,潘俊.2004.一种数字航空影像的匀光方法.中国图象图形学报,9(6):744-748)[DOI:10.3969/j.issn.1006-8961.2004.06.016]
Yao F,Wan Y C and Hu H.2013.Research on the improved image dodging algorithm based on Mask technique.Remote Sensing Information,28(3):8-13(姚芳,万幼川,胡晗.2013.基于Mask原理的改进匀光算法研究.遥感信息,28(3):8-13)[DOI:10.3969/j.issn.1000-3177.2013.03.002]
Yuan X X,Han Y T and Fang Y.2014.Improved Mask dodging algorithm for aerial imagery.Journal of Remote Sensing,18(3):630-641(袁修孝,韩宇韬,方毅.2014.改进的航摄影像Mask匀光算法.遥感学报,18(3):630-641)[DOI:10.11834/jrs.20143295]
Zhang Z,Zhu B S and Zhu S L.2010.An improved MASK dodging method based on contrast consistency processing.Journal of Geomatics Science and Technology,27(1):54-56(张振,朱宝山,朱述龙.2010.反差一致性改进的MASK匀光算法.测绘科学技术学报,27(1):54-56)[DOI:10.3969/j.issn.1673-6338.2010.01.014]
Zhang Z,Zhu B S,Zhu S L and Cao W.2009.Improved MASKdodging method based on wavelet.Journal of Remote Sensing,13(6):1074-1081(张振,朱宝山,朱述龙,曹闻.2009.小波变换改进的MASK匀光算法.遥感学报,13(6):1074-1081)[DOI:10.11834/jrs.20090607]
Zhang Z X,Li Z J,Zhang J Q and Zheng L.2003.Use discrete chromatic space to tune the image tone in a color image mosaic//Proceedings of the Third International Symposium on Multispectral Image Processing and Pattern Recognition.Beijing:SPIE[DOI:10.1117/12.538550]
Zhang Z X and Zou S B.2011.An improved algorithm of Mask image dodging for aerial image//Proceedings of MIPPR 2011:Remote Sensing Image Processing,Geographic Information Systems and Other Applications.Guilin:SPIE[DOI:10.1117/12.901869]
Zheng J,Xu C G,Xiao D G,Li H and Huang H.2003.The technique of digit image’s illumination uneven elimination.Transactions of Beijing Institute of Technology,23(3):285-289(郑军,徐春广,肖定国,理华,黄卉.2003.数字图像中照度不均匀校正技术研究.北京理工大学学报,23(3):285-289)[DOI:10.3969/j.issn.1001-0645.2003.03.006]
Zhu S L,Zhang Z,Zhu B S and Cao W.2011.Experimental comparison among five algorithms of brightness and contrast homogenization.Journal of Remote Sensing,15(1):111-122(朱述龙,张振,朱宝山,曹闻.2011.遥感影像亮度和反差分布不均匀性校正算法的效果比较.遥感学报,15(1):111-122)[DOI:10.11834/jrs.20110109]