基于指数变量的复小波域自适应扩散图像滤波算法

doi:10.11772/j.issn.1001-9081.2014.06.1758

计算机应用 ›› 2014, Vol. 34 ›› Issue (6): 1758-1761.DOI: 10.11772/j.issn.1001-9081.2014.06.1758

基于指数变量的复小波域自适应扩散图像滤波算法

刘金华

四川九洲电器集团有限责任公司卫星导航事业部,四川绵阳 621000

收稿日期:2013-12-09 修回日期:2014-02-16 发布日期:2014-07-02 出版日期:2014-06-01
通讯作者: 刘金华
作者简介:刘金华(1978-),男,江西贵溪人,博士,主要研究方向:稀疏信号处理、计算机视觉、机器学习。

Adaptive image diffusion filtering based on exponent variable in the complex wavelet domain

Jin-Hua LIU

Department of Satellite Navigation, Sichuan Jiuzhou Electric Group Company Limited, Mianyang Sichuan 621000, China

Received:2013-12-09 Revised:2014-02-16 Online:2014-07-02 Published:2014-06-01
Contact: Jin-Hua LIU

摘要/Abstract

摘要：

为了克服传统各项异性扩散模型在图像滤波时出现的阶梯效应和边缘模糊问题,利用复小波变换较好的完美重构性和方向选择性等特点,结合图像的梯度和复小波变换模特征,设计了一种复小波域自适应图像扩散滤波模型,提出了一种基于指数变量的自适应扩散图像滤波算法。通过计算机仿真验证了所提算法的滤波性能,结果表明该算法在低信噪比条件下可有效地滤除图像噪声,并且能较好地保持图像的边缘、纹理等细节信息。

Abstract:

In order to overcome the problem of staircase effect and edge blurring caused by using the traditional anisotropic diffusion model, the perfect reconstruction and better direction selectivity characteristics of the complex wavelet transform were applied to design an adaptive diffusion model combined with the gradient and complex wavelet transform modulus in the complex wavelet domain, and an adaptive image diffusion filtering algorithm was proposed based on exponent variable in this work. Finally, the filtering performance of the proposed algorithm was tested through computer simulation. The experimental results show that the noise can be filtered effectively in low Signal-to-Noise Ratio (SNR) conditions, and the edges and textures can be preserved well by the proposed method.

中图分类号:

TP391.41

刘金华. 基于指数变量的复小波域自适应扩散图像滤波算法[J]. 计算机应用, 2014, 34(6): 1758-1761.

Jin-Hua LIU. Adaptive image diffusion filtering based on exponent variable in the complex wavelet domain[J]. Journal of Computer Applications, 2014, 34(6): 1758-1761.

参考文献

[1]PERONA P, MALIK J. Scale-space and edge detection using anisotropic diffusion [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639.
[2]HE Y, YANG H, LI L, et al. Adaptive threshold denoising algorithm with neighboring window average energy based on contourlet transform[J]. , 2012,32(5): 1286-1288. (何雅丽,杨恢先,李利,等.邻域窗能量平均的Contourlet变换自适应阈值去噪算法[J]. 计算机应用, 2012,32(5): 1286-1288.)
[3]XIE K, ZHANG F. Image denoising method based on dictionary learning with union of two orthonormal bases[J]. , 2012,32(4): 1119-1121.(解凯,张芬.基于双正交基字典学习的图像去噪方法[J].计算机应用, 2012,32(4): 1119-1121.)
[4]KRISSIAN K, WESTIN C F, KIKINIS R. Oriented speckle reducing anisotropic diffusion [J]. IEEE Transactions on Image Processing, 2007, 16(5): 1412-1424.
[5]CATTE F, LIONS P L, MOREL J M, et al. Image selective smoothing and edge detection by nonlinear diffusion [J]. SIAM Journal of Numerical Analysis, 1992, 29(1): 182-193.
[6]YOU Y, KAVEH M. Fourth-order partial differential equationsfor noise removal [J]. IEEE Transactions on Image Processing, 2000, 9(10): 1723-1730.
[7]GILBOA G, SOCHEN N, ZEEVI Y Y. Forward-and backward diffusion processes for adaptive image enhancement and denoising [J]. IEEE Transactions on Image Processing, 2002, 11(7): 689-703.
[8]GILBOA G, SOCHEN N, ZEEVI Y Y. Image enhancement and denoising by complex diffusion process [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(8): 1020-1036.
[9]YU J H, WANG Y Y, SHEN Y Z. Noise reduction and edge detection via kernel anisotropic diffusion[J]. Pattern Recognition Letters, 2008, 29(10): 1496-1503.
[10]YU J, TAN J, WANG Y. Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method[J]. Pattern Recognition, 2010, 43(9): 3083-3092.
[11]GUO Z C, SUN J B, ZHANG D Z, et al. Adaptive Perona-Malik model based on the variable exponent for image denoising[J]. IEEE Transactions on Image Processing,2012, 21(3): 958-967.
[12]MASSIMO F, HO G H, YEONG H H. Noise reduction based on partial-reference, dual-tree complex wavelet transform shrinkage [J]. IEEE Transactions on Image Processing,2013, 22(5):1859-1871.
[13]YOU Y L,WEN YX. Behavioral analysis of anisotropic diffusion in image processing[J]. IEEE Transactions on Image Processing, 1996, 5(11): 1539-1552.
[14]LIU J. Research on some key issues for digital image watermarking in the transform domain [D]. Chengdu: University of Electronic Science and Technology of China, 2011：97-102.(刘金华.变换域数字图像水印若干关键技术研究[D].成都:电子科技大学,2011:97-102.)
[15]WANG Z, BOVIKA C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing,2004, 13(4): 600-612.
[16]RUDIN L, OSHER S, FATEMI E. Nonlinear total variation based noise removal algorithms [J]. Physical D, 1992, 60(1/2/3/4): 259-268.
[17]ELAD M, AHARON M. Image denoising via sparse and redundant representations over learned dictionaries [J]. IEEE Transactions on Image Processing,2006, 15(12):3736-3745.

基于指数变量的复小波域自适应扩散图像滤波算法

Adaptive image diffusion filtering based on exponent variable in the complex wavelet domain

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