K-SVD Dictionary Learning Algorithm Based on TL1Norm
YUAN Chao
LI Haiyang
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.
Keywords：dictionary learningK-SVDthreshold iterative algorithmTL1normimage reconstruction
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 2327-2331,2363 )