摘要
针对稀疏表示模型的过完备字典集训练过程中图像块采样不充分问题,提出图像组转置训练及非凸约束的去噪去模糊算法.采用组间方差约束的图像块搜索策略,并根据自适应软阈值对筛选的字典集进行转置学习.在重构过程中采用l p(0<p<1)范数约束以保证结果的强稀疏性.最后采用Bregman拆分迭代法求解文中非凸模型.实验表明,文中算法重构图像具有较好的视觉效果,去噪去模糊效果较优.
Aiming at insufficient sampling of image patches in the process of over-complete dictionary training of sparse representation model,an algorithm of image patch transform training and non-convex regularization for image denoising and deblurring is proposed.The image patch search strategy with inter-group variance constraint is adopted,and the selected dictionary set is transposed and learned according to the adaptive soft threshold.The l p(0<p<1)norm is adopted in the reconstruction process to ensure strong sparsity of the results.Split Bregman method is employed to solve the proposed non-convex model.Experimental results show that the proposed algorithm produces better visual effect and Denoising and Deblurring effect.
作者
杨平
赵燕伟
郑建炜
王万良
YANG Ping;ZHAO Yanwei;ZHENG Jianwei;WANG Wanliang(College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2019年第10期917-926,共10页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61602413,61873240)
浙江省自然科学基金面上项目(No.LY19F030016)资助~~
关键词
去噪
去模糊
转置学习
字典学习
非凸优化
Bregman拆分迭代
Denoising
Deblurring
Transform Learning
Dictionary Learning
Non-convex Optimization
Bregman Split Iteration
