改进的自适应广义整体变分图像降噪模型

doi:10.11772/j.issn.1001-9081.2016.06.1699

计算机应用 ›› 2016, Vol. 36 ›› Issue (6): 1699-1703.DOI: 10.11772/j.issn.1001-9081.2016.06.1699

改进的自适应广义整体变分图像降噪模型

高雷阜, 李超

辽宁工程技术大学理学院, 辽宁阜新 123000

收稿日期:2015-10-29 修回日期:2016-02-01 出版日期:2016-06-10 发布日期:2016-06-08
通讯作者: 李超
作者简介:高雷阜(1963-),男,辽宁阜新人,教授,博士,主要研究方向:非线性规划、混沌系统;李超(1984-),男,辽宁阜新人,博士研究生,主要研究方向:偏微分数字图像处理、管理信息系统。
基金资助:
高校博士学科点专项科研联合基金资助项目(20132121110009);国家自然科学基金青年基金资助项目(11401284)。

Improvement of adaptive generalized total variation model for image denoising

GAO Leifu, LI Chao

College of Science, Liaoning Technical University, Fuxin Liaoning 123000, China

Received:2015-10-29 Revised:2016-02-01 Online:2016-06-10 Published:2016-06-08
Supported by:
This work is partially supported by the Specialized Research Fund for the Doctoral Program of Higher Education (20132121110009), the National Natural Science Foundation of China (11401284).

摘要/Abstract

摘要： 针对自适应广义整体变分(AGTV)图像降噪模型对图像边缘信息定位精度不高及提取不足的问题,为提高图像降噪效果和峰值信噪比,提出了改进的AGTV(IAGTV)图像降噪模型。一方面,该算法换用精度更高的梯度计算方法,相对于AGTV更精确地定位图像边缘;另一方面,为优化图像预处理的滤波过程,用高斯-拉普拉斯联合变换替代高斯平滑滤波,更有利于检测图像边缘信息,在实现降噪的同时防止边缘信息弱化。数值仿真实验得出,IAGTV模型的复原图像峰值信噪比相对于固定p值的GTV模型提高了大约1.0 dB,比AGTV模型提高了至少0.2 dB。实验结果表明IAGTV具有良好的图像降噪能力。

关键词: 图像降噪, 边缘信息, 广义整体变分模型, 自适应, 梯度

Abstract: The Adaptive Generalized Total Variation (AGTV) model for image denoising has the shortages that it cannot locate image edge accurately and extract enough edge information. In order to improve the effectiveness and Peak Signal-to-Noise Ratio (PSNR) of image denoising, an Improved AGTV(IAGTV) model for image denoising was presented. On the one hand, another gradient calculating method with higher accuracy was adopted, in order to locate image edge more accurately than AGTV. On the other hand, for optimizing the filtering of image preprocess, the united Gauss-Laplace conversion which was good at image edge information detection was chosen to take place of Gaussian smoothing filter, so as to prevent edge information from reduction while denoising. Numerical simulation experiments show that the restored image PSNR of IAGTV was increased approximately by 1 dB than that of GTV with the fixed value p and at least 0.2 dB than that of AGTV. The experimental results show that IAGTV has good ability of image denoising.

Key words: image denoising, edge information, Generalized Total Variation (GTV) model, adaptive, gradient

中图分类号:

TP391.4

高雷阜, 李超. 改进的自适应广义整体变分图像降噪模型[J]. 计算机应用, 2016, 36(6): 1699-1703.

GAO Leifu, LI Chao. Improvement of adaptive generalized total variation model for image denoising[J]. Journal of Computer Applications, 2016, 36(6): 1699-1703.

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改进的自适应广义整体变分图像降噪模型

Improvement of adaptive generalized total variation model for image denoising

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