DocumentCode :
3666635
Title :
Research on image super-resolution reconstruction based on sparse representation
Author :
Jia Tong;Meng Hai Xiu
Author_Institution :
Northeastern University, College of Information Science and Engineering, Shen Yang
fYear :
2015
fDate :
6/1/2015 12:00:00 AM
Firstpage :
317
Lastpage :
320
Abstract :
Constructing an appropriate over-complete dictionary is the key problem of super-resolution reconstruction based on sparse representation. First, according to the maximum likelihood estimation principle, an isomorphic over-complete dictionary learning model based on mixture of Gauss is proposed. The model is described by the weight l2 norm and the weight matrix is designed by the residual. And the isomorphic coupled dictionary learning problem is translated into the single dictionary learning problem. Then, the dictionary is learned by the alternate and iterative strategy using sparse coding and dictionary updating. Finally, the dictionary is utilized in the process of super-resolution reconstruction. The experimental results test the effectiveness of the algorithm.
Keywords :
"Dictionaries","Image reconstruction","Image resolution","Signal resolution","Training","Sparse matrices","Encoding"
Publisher :
ieee
Conference_Titel :
Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015 IEEE International Conference on
Print_ISBN :
978-1-4799-8728-3
Type :
conf
DOI :
10.1109/CYBER.2015.7287955
Filename :
7287955
Link To Document :
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