A Similar K-SVD Optimization Algorithm Generalizing the K-Means and the Bayesian tracking
The last decade have seen tremendous improvement in the development of new image information processing and computational tools based on sparse representation.Today,in the information sciences,computer vision and image process-ing,the development of sparse representation algorithms led to convenient tools to transient compressed image (data) rapidly,to remove noise from image,and to get the super-resolution image.In the study of sparse representation of images,overcomplete dictionary is used.It contains prototype image-atoms.In this way,the images are described by sparse linear combinations of theses atoms.In this field has concentrated mainly on the design of a better dictionary.The generalized K-Means algorithm (K-SVD) 1 taught us a very good case.This paper has proposed an optimization algorithm adopting the Bayesian tracking and K-SVD analysis method.We analyze this algorithm and demonstrate its results on image data.
Sparese Repressentation Bayesina Prior K-SVD Atom decomposition,Dictionaty
Renjie WU S.Kamata
Graduate Schol of Information,Produvtion and systems Waseda Univercity Kitakyushu-shi,Japan
国际会议
杭州
英文
1708-1711
2013-03-22（万方平台首次上网日期，不代表论文的发表时间）