Multi-channel fast super-resolution image reconstruction based on matrix observation model  

作　　者：刘洪臣 冯勇 李林静 

机构地区：[1]School of Electrical Engineering and Automation,Harbin Institute of Technology

出　　处：《Journal of Harbin Institute of Technology(New Series)》2010年第2期239-246,共8页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the National Natural Science Foundation of China(Grant No.60474016);the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.2009046)

摘　　要：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.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 high- resolution 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.

关 键 词：SUPER-RESOLUTION image reconstruction tensor product wavelet fusion 

分 类 号：TN911.73[电子电信—通信与信息系统]