计算机与数字工程2017，Vol.45Issue(12)：2327-2331,2363,6.DOI:10.3969/j.issn.1672-9722.2017.12.001
基于TL1范数的改进K-SVD字典学习算法
K-SVD Dictionary Learning Algorithm Based on TL1Norm
摘要
Abstract
K-SVD dictionary learning algorithm is employed to obtain the training dictionary by using sparse coding and dic?tionary updating iteratively,in which Orthogonal Matching Pursuit algorithm(OMP)is used to get the sparse expressions in the sparse coding stage,while the SVD algorithm is utilized to update the dictionary.However,when it is applied into the image recon?struction,the Orthogonal Matching Pursuit algorithm(OMP)is slower and its accuracy is not satisfied.Aiming at this problem,To improve the speed and performance of training dictionary,l0is replaced with TL1in the sparse coding stage,and the iterative thresh?old algorithm is used to the sparse expressions.To test the performance of the proposed algorithm,date synthesis experiment is con?ducted under different sparse degree,and these results show that the proposed algorithm is better than the K-SVD.To further test the performance of the proposed algorithm,the standard image is used to simulate and the experimental results show that the pro?posed algorithm is faster than K-SVD to obtain the training dictionary,and has higher PSNR and better reconstruction performance.关键词
字典学习/KSVD/稀疏编码/阈值迭代算法/TL1范数/图像重构Key words
dictionary learning/K-SVD/threshold iterative algorithm/TL1norm/image reconstruction分类
信息技术与安全科学引用本文复制引用
袁超,李海洋..基于TL1范数的改进K-SVD字典学习算法[J].计算机与数字工程,2017,45(12):2327-2331,2363,6.基金项目
国家自然科学基金项目(编号:11271297) （编号:11271297）
陕西省自然科学基金项目(编号:2015JM1012)资助. （编号:2015JM1012）
