自适应全变分图像去噪模型及其快速求解(Matlab CODE)
2012-03-23 13:27
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Abstract
在联合冲击滤波器和非线性各向异性扩散滤波器对含噪图像做预处理的基础上,利用边缘检测算子选取自适应参数,构建能同时兼顾图像平滑去噪与边缘保留的自适应全变分模型,并基于Bregman迭代正则化方法设计了其快速迭代求解算法。实验结果表明,自适应去噪模型及其求解算法在快速去除噪声的同时保留了图像的边缘轮廓和纹理等细节信息,得到的复原图像在客观评价标准和主观视觉效果方面均有所提高。
Download
Paper (image denoising),In
Application Research of Computers. 2011, 28(12): 4797-4800.
[align=justify]Supplemental images(supplied many experimential images).[/align]
[align=justify]Mathlab codes..... coming soon (如果你需要该论文的程序代码,可以发送我邮箱lwflight'at' gmail.com)[/align]
References
Goldstein T, Osher S.The split Bregman method for L1-regularized problems[J]. SIAM Journal
on Imaging Sciences, 2009, 2(2): 323-343.
Jia R Q, Zhao H Q, Zhao W.Convergence analysis of the Bregman method for the variation model of image
denoising[J]. Applied and Computational Harmonic Analysis, 2009, 27(3): 367-379.
Jia R Q, Zhao H Q.A fast algorithm for the total variation model of image denoising[J]. Advances in
Computational Mathematics, 2010, 33(2): 231-241.
Almeida S C, Almeida L B.Blind and semi-blind deblurring of
natural images[J]. IEEE Transactions on Image Processing, 2010, 19(1): 36-52.
Zhou W, Alna C B, Hamid R S, et al.Image quality assessment: from error visibility to structural
similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
Note: Only a low-quality paper in a Chinese journal, please don't gibe me! :) Thanks! Nevertheless, I still hope all the friends can learn a lot from this paper or the references.
Especially for the students with research interests focusing on image processing and computer vision.
在联合冲击滤波器和非线性各向异性扩散滤波器对含噪图像做预处理的基础上,利用边缘检测算子选取自适应参数,构建能同时兼顾图像平滑去噪与边缘保留的自适应全变分模型,并基于Bregman迭代正则化方法设计了其快速迭代求解算法。实验结果表明,自适应去噪模型及其求解算法在快速去除噪声的同时保留了图像的边缘轮廓和纹理等细节信息,得到的复原图像在客观评价标准和主观视觉效果方面均有所提高。
Download
Paper (image denoising),In
Application Research of Computers. 2011, 28(12): 4797-4800.
[align=justify]Supplemental images(supplied many experimential images).[/align]
[align=justify]Mathlab codes..... coming soon (如果你需要该论文的程序代码,可以发送我邮箱lwflight'at' gmail.com)[/align]
References
Goldstein T, Osher S.The split Bregman method for L1-regularized problems[J]. SIAM Journal
on Imaging Sciences, 2009, 2(2): 323-343.
Jia R Q, Zhao H Q, Zhao W.Convergence analysis of the Bregman method for the variation model of image
denoising[J]. Applied and Computational Harmonic Analysis, 2009, 27(3): 367-379.
Jia R Q, Zhao H Q.A fast algorithm for the total variation model of image denoising[J]. Advances in
Computational Mathematics, 2010, 33(2): 231-241.
Almeida S C, Almeida L B.Blind and semi-blind deblurring of
natural images[J]. IEEE Transactions on Image Processing, 2010, 19(1): 36-52.
Zhou W, Alna C B, Hamid R S, et al.Image quality assessment: from error visibility to structural
similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
Note: Only a low-quality paper in a Chinese journal, please don't gibe me! :) Thanks! Nevertheless, I still hope all the friends can learn a lot from this paper or the references.
Especially for the students with research interests focusing on image processing and computer vision.
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