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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:LIU Hong-chen,Feng Yong,LI Lin-jing.Multi-channel fast super-resolution image reconstruction based on matrix observation model[J].Journal of Harbin Institute Of Technology(New Series),2010,17(2):239-246.DOI:10.11916/j.issn.1005-9113.2010.02.018.
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Multi-channel fast super-resolution image reconstruction based on matrix observation model
Author NameAffiliation
LIU Hong-chen School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China 
Feng Yong School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China 
LI Lin-jing School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China 
Abstract:
A multi-channel fast super-resolution image reconstruction algorithm based on matrix observation model is proposed in the paper,which consists of three steps to avoid the computational complexity: a single image SR reconstruction step,a registration step and a wavelet-based image fusion. This algorithm decomposes two large matrixes to the tensor product of two little matrixes and uses the natural isomorphism between matrix space and vector space to transform cost function based on matrix-vector products model to matrix form. Furthermore,we prove that the regularization part can be transformed to the matrix formed. The conjugate-gradient method is used to solve this new model. Finally,the wavelet fusion is used to integrate all the registered highresolution images obtained from the single image SR reconstruction step. The proposed algorithm reduces the storage requirement and the calculating complexity,and can be applied to large-dimension low-resolution images.
Key words:  super-resolution  image reconstruction  tensor product  wavelet fusion
DOI：10.11916/j.issn.1005-9113.2010.02.018
Clc Number:TP391.41
Fund:

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