基于稀疏表示与非局部相似的图像去噪算法

doi:10.11772/j.issn.1001-9081.2016.02.0551

计算机应用 ›› 2016, Vol. 36 ›› Issue (2): 551-555.DOI: 10.11772/j.issn.1001-9081.2016.02.0551

基于稀疏表示与非局部相似的图像去噪算法

赵井坤^1,2, 周颖玥^1,2, 林茂松^1,2

1. 西南科技大学信息工程学院, 四川绵阳 621010;
2. 特殊环境机器人技术四川省重点实验室(西南科技大学), 四川绵阳 621010

收稿日期:2015-07-20 修回日期:2015-09-28 出版日期:2016-02-10 发布日期:2016-02-03
通讯作者: 赵井坤(1990-),男,山东泰安人,硕士研究生,主要研究方向:数字图像处理。
作者简介:周颖玥(1983-),女,四川马尔康人,讲师,博士,主要研究方向:图像处理与分析;林茂松(1964-),男,安徽滁州人,教授,博士,CCF会员,主要研究方向:计算机图形图像处理、科学计算可视化。
基金资助:
国家自然科学基金资助项目(61401379);四川省教育厅项目(14ZB0107);西南科技大学博士基金项目(13zx7148)。

Image denoising algorithm based on sparse representation and nonlocal similarity

ZHAO Jingkun^1,2, ZHOU Yingyue^1,2, LIN Maosong^1,2

1. School of Information Engineering, Southwest University of Science and Technology, Mianyang Sichuan 621010, China;
2. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province(Southwest University of Science and Technology), Mianyang Sichuan 621010, China

Received:2015-07-20 Revised:2015-09-28 Online:2016-02-10 Published:2016-02-03

摘要/Abstract

摘要： 针对受加性高斯白噪声(AWGN)与椒盐噪声(SPIN)以及随机值冲击噪声(RVIN)组成的混合噪声污染的图像进行去噪的问题,提出一种在现有加权编码算法的基础上将图像稀疏表示和非局部相似先验融合的改进算法。首先,利用基于字典的图像稀疏表示构建去噪变分模型,对模型中的数据保真项设计一个权重因子来抑制冲击噪声的干扰;其次,利用非局部平均思想对混合噪声图像进行初始去噪,在得到的图像中构建掩膜矩阵将冲击噪声点排除进而求取非局部相似先验知识;最后,将非局部相似先验与稀疏先验融合进变分模型的正则项中,求解变分模型得到最终去噪图像。实验结果表明,在不同的噪声比率下,所提算法与模糊加权非局部平均算法相比,峰值信噪比(PSNR)提高了1.7 dB,特征相似性指数(FSIM)提高了0.06;与加权编码算法相比,PSNR提高了0.64 dB,FSIM提高了0.03。该算法对于纹理较强的图像可以显著提升去噪效果,能有效地保留图像的本真信息。

关键词: 图像去噪, 混合噪声, 稀疏表示, 非局部相似, 变分模型

Abstract: For the problem of denoising images corrupted by mixed noise such as Additive White Gaussian Noise (AWGN) with Salt-and-Pepper Impulse Noise (SPIN) and Random-Valued Impulse Noise (RVIN), an improved image restoration algorithm on the basis of the existing weighted encoding method was proposed. The image priors about sparse representation and non-local similarity were integrated. Firstly, the sparse representation based on the dictionary was used to build a variational denoising model and a weighting factor was designed for data fidelity term to suppress impulse noise. Secondly, the method of non-local means was used to get an initialized denoised image and then a mask matrix was built to remove impulse noise points to get the good non-local similarity prior knowledge. Finally, the image sparsity prior and non-local similarity prior were integrated into the regularization of the variational model. The final denoised image was obtained by solving the variational model. The experimental results show that in different noise ratios, the Peak Signal-to-Noise Ratio (PSNR) of the proposed algorithm increased 1.7 dB than that of fuzzy weighted non-local means filter, and the Feature Similarity Index (FSIM) increased 0.06. Compared with weighted encoding method, the PSNR increased 0.64 dB, and the FSIM increased 0.03. The proposed method has better recovery performance especially for the texture strong images and can retain real information of the image.

Key words: image denoising, mixed noise, sparse representation, non-local similarity, variational model

中图分类号:

TP391.41

赵井坤, 周颖玥, 林茂松. 基于稀疏表示与非局部相似的图像去噪算法[J]. 计算机应用, 2016, 36(2): 551-555.

ZHAO Jingkun, ZHOU Yingyue, LIN Maosong. Image denoising algorithm based on sparse representation and nonlocal similarity[J]. Journal of Computer Applications, 2016, 36(2): 551-555.

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基于稀疏表示与非局部相似的图像去噪算法

Image denoising algorithm based on sparse representation and nonlocal similarity

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