基于SURE无偏估计自适应字典学习图像去噪算法  

Adaptive Dictionary Learning Image Denoising Algorithm Based on Stein-unbiased Risk Estimator

作　　者：张真真 龚玲 张新朝 ZHANG Zhenzhen;GONG Ling;ZHANG Xinchao(College of Information engineering,Zhengzhou Information Engineering Vocational College,Zhengzhou 450000,China;College of Information engineering,Zhengzhou University of Science & Technology,Zhengzhou 450000,China)

机构地区：[1]郑州信息工程职业学院信息工程学院,郑州450000 [2]郑州科技学院信息工程学院,郑州450000

出　　处：《湖北民族学院学报（自然科学版）》2019年第1期77-81,共5页Journal of Hubei Minzu University(Natural Science Edition)

基　　金：国家科技支撑计划项目(2015BAK01B06);国家自然科学基金项目(41401466);河南省科技发展计划项目(142102310247;172172310666)

摘　　要：要针对以往图像去噪算法存在阈值选取仍是经验值,而非函数最优解,缺乏系统化的理论方法等问题,因此提出基于SURE无偏估计的自适应字典学习图像去噪算法.首先通过第二代Bandelet变换获得最优的几何方向,运用四叉树计算最佳几何流,从而得到Bandelet图像块,然后运用SURE无偏估计自动获得全局最优解,最后运用KSVD来训练字典,获得图像块相对应的字典.实验结果表明运用SURE无偏估计进行阈值选取,使目标函数连续可微易求导,针对平滑图像很好的去除了大量伪边缘和"块效应",使图像的连通性更加明显.In view of the previous image denoising algorithm,the threshold selection is still the empirical value,not the function optimal solution,and the lack of systematic theoretical methods.Therefore,an adaptive dictionary learning image denoising algorithm based on SURE unbiased estimation is proposed.Firstly,the second-generation Bandelet transform can adaptively select the optimal geometric direction,and the quadratic tree segmentation is used to calculate the optimal geometric flow to obtain the Bandelet image block.Then the SURE unbiased risk estimate is used to automatically obtain the global optimal solution.Finally,the global optimal solution is obtained.K-SVD trains the dictionary and obtains a dictionary corresponding to the image block.The experimental results show that the Stein-unbiased risk estimator is used to select the threshold,so that the objective function can be easily derivable.A large number of pseudo-edges and"blockiness"are well removed for smooth images,which makes the image connectivity more obvious.

关 键 词：学习字典 第二代Bandelet K-SVD SURE无偏估计 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]