基于小波域的Contourlet变换法的自适应光学图像去噪算法研究

李东明; 盖梦野; 李超然; 张丽娟

doi:doi:10.3788/lop52.111001

激光与光电子学进展, 2015, 52 (11): 111001, 网络出版: 2015-10-15

基于小波域的Contourlet变换法的自适应光学图像去噪算法研究下载： 566次

Research on Adaptive Optics Image Denoising Algorithm Based on the Wavelet-Based Contourlet Transform

论文大纲

李东明 ^1,*盖梦野 ¹李超然 ¹张丽娟 ²

作者单位

¹ 吉林农业大学信息技术学院, 吉林长春 130118

² 长春工业大学计算机科学与工程学院, 吉林长春 130012

图像处理图像去噪 Contourlet 变换小波域贝叶斯萎缩法 image processing image denoising Contourlet transform wavelet domain BayesShrink

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摘要

从图像噪声的统计特性出发，结合贝叶斯萎缩法(BayesShrink)原理，提出了基于小波域的Contourlet 变换法的图像去噪方法。根据贝叶斯准则估计阈值Ti,j，并考虑邻域局部相关性，改进阈值的自适应选取方法，获得最优阈值 Ti,j[σX (LD)]，实现图像去噪，同时分析文中算法的去噪的峰值信噪比(PSNR)和计算的复杂度。仿真实验证明，与DWT-NABayesShrink 去噪方法、DTCWT-BayesShrink 和CbATD 去噪方法相比，视觉效果和PSNR 都有明显提高。

Abstract

Based on the statistical property of image noise and combining with BayesShrink theory, a method of image denoising based on wavelet domain Contourlet transform is presented. Using BayesShrink theory to estimate the threshold, considering the local correlation of the neighborhood, then improving the adaptive method of selecting threshold, finally obtaining the optimal threshold Ti,j [σX(LD)], this algorithm has implement the image denoising. Furthermore, analyzing the peak signal to noise ratio (PSNR) and its computational complexity. The simulation results show that the superiority of this algorithm which has obviously improved the visual effect and PSNR when compared to DWT- NABayesShrink method, DTCWT- BayesShrink method and CbATD method.

参考文献

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李东明, 盖梦野, 李超然, 张丽娟. 基于小波域的Contourlet变换法的自适应光学图像去噪算法研究[J]. 激光与光电子学进展, 2015, 52(11): 111001. Li Dongming, Gai Mengye, Li Chaoran, Zhang Lijuan. Research on Adaptive Optics Image Denoising Algorithm Based on the Wavelet-Based Contourlet Transform[J]. Laser & Optoelectronics Progress, 2015, 52(11): 111001.

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