米字型各向异性扩散模型的图像去噪算法

doi:10.11772/j.issn.1001-9081.2014.05.1494

计算机应用 ›› 2014, Vol. 34 ›› Issue (5): 1494-1498.DOI: 10.11772/j.issn.1001-9081.2014.05.1494

米字型各向异性扩散模型的图像去噪算法

翟东海¹,²,鱼江²,段维夏²,肖杰³,李帆¹

1. 西藏大学工学院，拉萨 850000
2. 西南交通大学信息科学与技术学院，成都 610031
3. 西南交通大学信息科学与技术学院, 成都 610031;

收稿日期:2013-10-31 修回日期:2013-12-21 发布日期:2014-05-30 出版日期:2014-05-01
通讯作者: 翟东海
作者简介:翟东海(1974-)，男，山西芮城人，副教授，博士，CCF会员〖BP(〗(E200030751M)〖BP)〗，主要研究方向：数字图像处理、海量数据挖掘；鱼江(1990-)，男，甘肃陇南人，硕士研究生，主要研究方向：数字图像处理；段维夏(1990-)，女，河南济源人，硕士研究生，主要研究方向：数字图像处理；肖杰(1988-)，女，四川成都人，硕士研究生，主要研究方向：数字图像处理；李帆(1991-)，男，陕西宝鸡人，主要研究方向：数字图像处理。
基金资助:
国家社会科学基金项目;国家级大学生创新创业训练计划项目;西藏自治区重点科技计划项目

Improved image denoising algorithm using UK-flag shaped anisotropic diffusion model

ZHAI Donghai¹,²,YU Jiang²,DUAN Weixia³,XIAO Jie²,LI Fan¹

1. Engineering School, Tibet University, Lhasa Tibet 850000, China
2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu Sichuan 610031, China;
3. School of Information Science and Technology, Southwest Jiaotong University, Chengdu Sichuan 610031, China

Received:2013-10-31 Revised:2013-12-21 Online:2014-05-30 Published:2014-05-01
Contact: ZHAI Donghai

摘要/Abstract

摘要：

针对原始的各向异性扩散模型在对带噪图像去噪时，只利用了邻域内东、南、西、北4个方向上的参考信息，使得去噪效果不够明显的问题，提出了米字型各向异性扩散模型的图像去噪算法。该算法在利用了原始算法中待修复点周围4个方向上参考信息的基础上，还引入了该点邻域内对角线方向上的新信息，给出了采用周围8个方向上的信息进行对图像去噪的新模型，同时证明了该模型的合理性。用新提出的算法与原算法以及一种改进的同类算法对4幅带噪图像进行去噪。实验结果表明，新提出算法去噪效果的峰值信噪比(PSNR)相比原算法和改进同类算法平均提高1.90dB和1.43dB，平均结构相似度(MSSIM)分别平均提高0.175和0.1，说明该算法更适合于图像去噪。

Abstract:

To effectively improve the denoising effect of the original anisotropic diffusion model that used only the 4 neighborhood pixels information and ignored the diagonal neighborhood pixels information of the pixel to be repaired in the image denoising process, a image denoising algorithm using UK-flag shaped anisotropic diffusion model was proposed. This model not only made full use of the reference information of the 4 neighborhood pixels as in original algorithm, but also used another 4 diagonal neighborhood pixels information in the denoising process. Then the model using the 8 direction pixels information for image denoising was presented, and it was proved to be rational. The proposed algorithm, the original algorithm, and an improved similar algorithm were used to remove the noise from 4 images with noise. The experimental results show that the proposed algorithm has an average increase of 1.90dB and 1.43dB in Peak Signal-to-Noise Ratio (PSNR) value respectively, and an average increase of 0.175 and 0.1 in Mean Structure Similitary Index (MSSIM) value respectively, compared with the original algorithm and the improved similar algorithm, which concludes that the proposed algorithm is more suitable for image denoising. algorithm not only made full use of the reference information of the 4 neighborhood pixels as in original algorithm, but also another 4 diagonal neighborhood pixels information was used in the denoising process, and the algorithm was proved to be rationality. The experimental results showed that the proposed algorithm could increase the PSNR (peak signal-to-noise ratio) value 1.69db, and the MSSIM(mean structure similitary index) value 0.14, compared with the other similar algorithms in image denoising, which conclud that this proposed algorithm is more suitable for image denoising.

中图分类号:

TP391.41

翟东海鱼江段维夏肖杰李帆. 米字型各向异性扩散模型的图像去噪算法[J]. 计算机应用, 2014, 34(5): 1494-1498.

ZHAI Donghai YU Jiang DUAN Weixia XIAO Jie LI Fan. Improved image denoising algorithm using UK-flag shaped anisotropic diffusion model[J]. Journal of Computer Applications, 2014, 34(5): 1494-1498.

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米字型各向异性扩散模型的图像去噪算法

Improved image denoising algorithm using UK-flag shaped anisotropic diffusion model

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