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

作　　者：高雷阜[1] 李超[1] 

机构地区：[1]辽宁工程技术大学理学院,辽宁阜新123000

出　　处：《计算机应用》2016年第6期1699-1703,1750,共6页journal of Computer Applications

基　　金：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)~~

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 d B,比AGTV模型提高了至少0.2 d B.实验结果表明IAGTV具有良好的图像降噪能力.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-toNoise 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 d B than that of GTV with the fixed value p and at least 0. 2 d B than that of AGTV. The experimental results show that IAGTV has good ability of image denoising.

关 键 词：图像降噪 边缘信息 广义整体变分模型 自适应 梯度 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]