基于剪切波和改进P-M扩散的图像去噪

doi:10.3969/j.issn.1006-2475.2015.05.011

计算机与现代化

基于剪切波和改进P-M扩散的图像去噪

(河南工业职业技术学院计算机工程系，河南南阳 473009)

收稿日期:2015-01-12 出版日期:2015-05-18 发布日期:2015-05-18
作者简介:李垒(1982-)，男，河南南阳人，河南工业职业技术学院计算机工程系讲师，硕士，研究方向：计算机软件与图像处理; 任越美(1984-)，女，讲师，博士研究生，研究方向：图像处理与模式识别。
基金资助:
国家自然科学基金资助项目(61231016, 61301192, 61303123); 河南省科技计划项目(142102210557)

Image Denoising Based on Shearlet Transform and Improved Anisotropic Diffusion

(Department of Computer Engineering, Henan Polytechnic Institute, Nanyang 473009, China)

Received:2015-01-12 Online:2015-05-18 Published:2015-05-18

摘要/Abstract

摘要： 针对多尺度几何分析方法去噪时产生的伪Gibbs效应和各向异性扩散模型产生的阶梯效应，提出一种基于剪切波的改进各向异性扩散图像去噪方法。首先对噪声图像进行剪切波变换得到不同尺度的系数矩阵，然后利用改进的各向异性扩散方程对变换后的系数进行处理，实现建立在对图像精细分析Gibbs效应，取得良好的视觉效果。基础上的各向异性扩散模型。实验结果表明，该方法能较好地抑制噪声和保持边缘，同时有效地抑制伪

关键词: 图像去噪, 剪切波变换, 各向异性扩散, 多尺度分析, 阶梯效应

Abstract: To suppress the pseudo-Gibbs in multi-scale geometric analysis denoising and alert the staircase effect in anisotropic diffusion effectively, an image denoising method combining shearlet transform and improved anisotropic diffusion is proposed. Firstly, the noisy image is decomposed by shearlet transform, and different scale coefficient matrices are obtained. Then, the improved anisotropic diffusion equation is applied to process these shearlet coefficients, which realized a new anisotropic diffusion denoising based on multi-scale analysis of the image. Finally, the clean image is obtained from the processed shearlet coefficients. Experiments show that the proposed method can suppress the pseudo-Gibbs and staircase effect, as well as reduce the noise and preserve the detail better.

Key words: image denoising, shearlet transform, anisotropic diffusion, multi-scale analysis, staircase effect

中图分类号:

TP391

李垒，任越美. 基于剪切波和改进P-M扩散的图像去噪[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2015.05.011.

LI Lei, REN Yue-mei. Image Denoising Based on Shearlet Transform and Improved Anisotropic Diffusion[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.05.011.

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基于剪切波和改进P-M扩散的图像去噪

Image Denoising Based on Shearlet Transform and Improved Anisotropic Diffusion

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