自适应加权的二阶总广义变分图像去噪  

Adaptive Weighted Second Order Total Generalized Variation Image De-noising

作　　者：马晓月 赵勋杰 MA Xiao-yue;ZHAO Xun-jie(School of Optoelectronic Science and Engineering of Soochow University,Suzhou 215006,China)

机构地区：[1]苏州大学光电科学与工程学院,江苏苏州215006

出　　处：《光电技术应用》2018年第4期31-34,78,共5页Electro-Optic Technology Application

摘　　要：针对全变分(total variation,TV)模型在图像去噪过程中易于产生"阶梯效应"的缺点,提出了一种改进的二阶总广义变分(total generalized variation,TGV)图像去噪模型。新模型中,利用Kirsch边缘检测算子提取到的图像纹理信息,在二阶TGV去噪模型的正则项中引入一个边缘指示函数引导扩散。实验表明,与经典的TV去噪模型和二阶TGV去噪模型相比,新模型无论是在视觉效果上还是在峰值信噪比(PSNR)和均方误差(MSE)方面都有明显的改善,在有效地去除噪声的同时自适应地保护图像的边缘信息和细小的纹理结构信息。Aiming at the drawback of the classical total variation(TV)de-noising model in which the staircase effect is often produced,an image de-noising model based on improved second order total generalized variation(TGV)is proposed.In the new model,the image texture information extracted by Kirsch edge detection operator is used to introduce an edge indicator function to guide diffusion in the regularization term of second order TGV.The experiment shows that compared with the classical TV de-noising model and the second-order TGV de-noising mod?el,the new model has obvious improvement in both visual effect,peak signal-to-noise ratio(PSNR)and mean square error(MSE),which can remove the noise effectively while protecting image edge information and fine texture structure information adaptively.

关 键 词：全变分(TV)模型 阶梯效应 二阶总广义变分(TGV)模型 Kirsch边缘检测算子 

分 类 号：TP391.413[自动化与计算机技术—计算机应用技术]