一种去除遥感影像混合噪声的集成BM3D方法
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摘要
BM3D(block matching and 3D filtering)是一种有效的高斯噪声去除方法,但对遥感影像中常含有的高斯和脉冲混合噪声去除效果具有局限性;而集成方法是去除混合噪声的有效方式。针对BM3D去噪的缺点,结合其去噪优势,研究发展了一种集成BM3D方法,并改进了一种噪声量估算方法M-Liu法,用于先验噪声估算,作为算法的输入参数。算法验证结果表明,集成BM3D法具有较好的去噪特性,能兼顾影像噪声去除和细节信号的保留,优于同类方法,可为图像去噪提供一种新的方法,对于遥感影像后期应用性研究具有一定的意义。
BM3D(block matching and 3D filtering) method is an effective Gaussian noise removing method, but it shows limitation when removing Gaussian and impulse mixed noise in remote sensing images. The integrated method is a more effective way to remove mixed noise. In order to make full use of the advantages of BM3D method, an improved integration method(integrated BM3D method) has been developed. And a modified method M-Liu method is raised and used for estimating the priori noise level as an input parameter for the integrated BM3D method. According to algorithm validation, this paper draws the conclusion that the integrated BM3D method, taking the removal of image noise and the retention of detail signals into account, has good denoising performance. The method can be used to provide a new method for image denoising, and it is useful for the later application of remote sensing image.
BM3D(block matching and 3D filtering) method is an effective Gaussian noise removing method, but it shows limitation when removing Gaussian and impulse mixed noise in remote sensing images. The integrated method is a more effective way to remove mixed noise. In order to make full use of the advantages of BM3D method, an improved integration method(integrated BM3D method) has been developed. And a modified method M-Liu method is raised and used for estimating the priori noise level as an input parameter for the integrated BM3D method. According to algorithm validation, this paper draws the conclusion that the integrated BM3D method, taking the removal of image noise and the retention of detail signals into account, has good denoising performance. The method can be used to provide a new method for image denoising, and it is useful for the later application of remote sensing image.
引文
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[2] Liu Xiaoli,Ren Liqiu,Li Wei,et al.Threshold Optimized Wavelet for Remotely Sensed Image Denoising[J].Remote Sensing Information,2016,31(2):109-113(刘晓莉,任丽秋,李伟,等.阈值优化的遥感影像小波去噪[J].遥感信息,2016,31(2):109-113)
[3] Li Jianping.Wavelet Analysis and Signal Processing[M].Chongqing:Chongqing Press,1997(李建平.小波分析与信号处理[M].重庆:重庆出版社,1997)
[4] Dabov K,Foi A,Katkovnik V,et al.Image Denoising by Sparse 3-D Transform-domain Collaborative Filtering[J].IEEE Transactions on Image Processing,2007,16(8):2 080-2 095
[5] He Kun,Ju Shenggen,Lin Tao,et al.Image Denoising on TV Numerical Computation[J].Journal of University Electronic Science and Technology of China,2013,42(3):459-463(何坤,琚生根,林涛,等.TV数值计算的图像去噪[J].电子科技大学学报,2013,42(3):459-463)
[6] Laine A F.Wavelet Applications in Signal and Ima-ge Processing[M].Washington D C:SPIE-the International Society for Optical Engineering,2000
[7] Xia Qin,Xing Shuai,Ma Dongyang,et al.An Improved K-SVD Based Denoising Method for Remote Sensing Satellite Images[J].Journal of Remote Sensing,2016,20(3):441-449(夏琴,邢帅,马东洋,等.遥感卫星影像K-SVD稀疏表示去噪[J].遥感学报,2016,20(3):441-449
[8] Muresan D D,Parks T W.Adaptive Principal Components and Image Denoising[C].International Conference on Image Processing,IEEE,Spain,2003
[9] Elad M,Aharon M.Image Denoising Via Sparse and Redundant Representations over Learned Dictionaries[J].IEEE Transactions on Image Proces-sing,2006,15(12):3 736-3 745
[10] Yuan Wencheng,Yang Dexing,Chen Chao.A Synthetic Filtering Method for Restoration of Images Contaminated by Mixed Noise[J].Microprocessors,2007,28(4):78-80(袁文成,杨德兴,陈超.图像混合噪声的一种组合滤波消除方法[J].微处理机,2007,28(4):78-80)
[11] Dong H,Wang F.Image-Denoising Based on Bior Wavelet Transform and Median Filter[C].Photonics and Optoelectronics (SOPO),Shanghai,China,2012
[12] Zhu Jianjun,Zhou Jinghong,Zhou Cui,et al.A New Combination Filtering Method to Remove Mixed Noise of Remote Sensing Images[J].Geomatics and Information Science of Wuhan University,2017,42(3):348-354(朱建军,周靖鸿,周璀,等.一种新的去除遥感影像混合噪声组合滤波方法[J].武汉大学学报·信息科学版,2017,42(3):348-354)
[13] Donoho D L.De-noising by Soft-Thresholding[J].IEEE Transactions on Information Theory,1995,41(3):613-627
[14] Donoho D L,Johnstone I M.Adapting to Unknown Smoothness via Wavelet Shrinkage[J].Journal of the American Statistical Association,1995,90(432):1 200-1 224
[15] Lim J S.Two-dimensional Signal and Image Processing[M].Upper Saddle River:Prentice Hall,1990
[16] Aja-Fernández S,Alberola-López C.Automatic Noise Estimation in Images Using Local Statistics.Additive and Multiplicative Cases[J].Image and Vision Computing,2009,27(6):756-770
[17] Pyatykh S,Hesser J,Zheng L.Image Noise Level Estimation by Principal Component Analysis[J].IEEE Transactions on Image Processing,2013,22(2):687
[18] Liu X,Tanaka M,Okutomi M.Noise Level Estimation Using Weak Textured Patches of a Single Noisy Image[C].Image Processing (ICIP),19th IEEE International Conference on,IEEE,Orlando,USA,2012
[19] Schneider C,Gürenci J.Mathematical Problems in Image Processing:Partial Differential Equations and the Calculus of Variations[M].New York:Springer,2009
[20] Ichigaya A,Nishida Y,Nakasu E.Nonreference Method for Estimating PSNR of MPEG-2 Coded Video by Using DCT Coefficients and Picture Energy[J].IEEE Transactions on Circuits & Systems for Video Technology,2008,18(6):817-826
[21] Salman A G,Kanigoro B,Rojali,et al.Application Hiding Messages in JPEG Images with the Method of Bit-Plane Complexity Segmentation on Android-Based Mobile Devices[C].Elsevier Ltd,Jakarta D C,2012