基于变指数的片相似性扩散图像降噪算法

doi:10.11772/j.issn.1001-9081.2014.10.2963

计算机应用 ›› 2014, Vol. 34 ›› Issue (10): 2963-2966.DOI: 10.11772/j.issn.1001-9081.2014.10.2963

基于变指数的片相似性扩散图像降噪算法

董婵婵¹,张权¹,郝慧艳¹,张芳¹,刘祎¹,孙未雅¹,桂志国¹,²

1. 电子测试技术国家重点实验室(中北大学)，太原 030051；
2. 仪器科学与动态测试教育部重点实验室(中北大学)，太原 030051

收稿日期:2014-04-02 修回日期:2014-05-24 发布日期:2014-10-30 出版日期:2014-10-01
通讯作者: 桂志国
作者简介:董婵婵(1988-),女,山东金乡人,硕士研究生,主要研究方向:基于偏微分的图像降噪;
张权(1974-),男,山西大同人,副教授,博士研究生,主要研究方向:图像处理与可视化;
郝慧艳(1980-),女,山西武乡人,副教授,博士,主要研究方向:信号分析、图像处理;
张芳(1989-),女,山西朔州人,硕士研究生,主要研究方向:低剂量CT重建;
刘祎(1987-),女,河南睢县人,博士研究生,主要研究方向:医学图像处理和重建;
孙未雅(1991-),女,河北唐山人,硕士研究生,主要研究方向:图像重建;
桂志国(1972-),男,天津蓟县人,教授,博士,主要研究方向:信号与信息处理、图像处理和识别。
基金资助:
国家自然科学基金资助项目;山西省国际合作项目;山西省高等学校优秀青年学术带头人支持计划项目;山西省高等学校优秀青年学术带头人支持计划项目;中北大学第十届研究生科技基金项目

Patch similarity anisotropic diffusion algorithm based on variable exponent for image denoising

DONG Chanchan¹,ZHANG Quan¹,HAO Huiyan¹,ZHANG Fang¹,LIU Yi¹,SUN Weiya¹,GUI Zhiguo¹,²

1. National Key Laboratory for Electronic Measurement Technology (North University of China), Taiyuan Shanxi 030051, China;
2. Key Laboratory of Instrumentation Science and Dynamic Measurement of Ministry of Education (North University of China), Taiyuan Shanxi 030051, China

Received:2014-04-02 Revised:2014-05-24 Online:2014-10-30 Published:2014-10-01
Contact: GUI Zhiguo

摘要/Abstract

摘要：

针对图像去噪过程中存在边缘保持与噪声抑制之间的矛盾，提出了一种基于变指数的片相似性扩散图像降噪算法。算法基于变指数的自适应降噪模型，引入片相似性的思想，构造出新的边缘检测算子和扩散系数函数。传统的各项异性扩散图像降噪算法利用单个像素点的灰度相似性（或梯度信息）检测边缘，不能很好地保持图像的弱边缘和纹理信息。而所提算法利用邻域像素的灰度相似性，可以在滤除图像噪声的同时，保持更多的细节信息。仿真结果表明，与其他传统的基于偏微分方程（PDE）的图像降噪算法相比，该算法将信噪比（SNR）和峰值信噪比（PSNR）提高至16.602480dB和31.284672dB，具有良好的抗噪性；同时视觉效果较好，保持了更多的弱边缘和纹理等细节特征，在噪声抑制与边缘保持之间取得了较好的权衡。

Abstract:

Concerning the contradiction between edge-preserving and noise-suppressing in the process of image denoising, a patch similarity anisotropic diffusion algorithm based on variable exponent for image denoising was proposed. The algorithm combined adaptive Perona-Malik (PM) model based on variable exponent for image denoising and the idea of patch similarity, constructed a new edge indicator and a new diffusion coefficient function. The traditional anisotropic diffusion algorithms for image denoising based on the intensity similarity of each single pixel (or gradient information) to detect edge cannot effectively preserve weak edges and details such as texture. However, the proposed algorithm can preserve more detail information while removing the noise, since the algorithm utilizes the intensity similarity of neighbor pixels. The simulation results show that, compared with the traditional image denoising algorithms based on Partial Differential Equation (PDE), the proposed algorithm improves Signal-to-Noise ratio (SNR) and Peak-Signal-to-Noise Ratio (PSNR) to 16.602480dB and 31.284672dB respectively, and enhances anti-noise capability. At the same time, the filtered image preserves more detail features such as weak edges and textures and has good visual effects. Therefore, the algorithm achieves a good balance between noise reduction and edge maintenance.

中图分类号:

TP391.41

董婵婵张权郝慧艳张芳刘祎孙未雅桂志国. 基于变指数的片相似性扩散图像降噪算法[J]. 计算机应用, 2014, 34(10): 2963-2966.

DONG Chanchan ZHANG Quan HAO Huiyan ZHANG Fang LIU Yi SUN Weiya GUI Zhiguo. Patch similarity anisotropic diffusion algorithm based on variable exponent for image denoising[J]. Journal of Computer Applications, 2014, 34(10): 2963-2966.

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基于变指数的片相似性扩散图像降噪算法

Patch similarity anisotropic diffusion algorithm based on variable exponent for image denoising

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