Image Super-Resolution Reconstruction via Improved Dictionary Learning Based on Coupled Feature Space
ZHAN Shu1, FANG Qi1, YANG Fu-meng2, CHANG Le-le1, YAN Ting1
1. School of Computer & Information, Hefei University of Technology, Hefei, Anhui 230009, China;
2. School of Electronic Information Engineering, Sanjiang University, Nanjing, Jiangsu 210012, China
Image super-resolution reconstruction via Improved Dictionary Learning based on Coupled Feature Space is studied in the paper, in order to solve the following problems:1 the dictionary training process is time-consuming, 2 the results are not satisfactory in the existing algorithms.In the proposed algorithm, at first, the Gaussian mixture model clustering algorithm is employed to cluster the training image blocks, secondly, quickly obtain high and low resolution feature space of dictionary and mapping matrix by using dictionary updating based on improved KSVD dictionary learning algorithm, and then, the Super-Resolution image is reconstructed according to the likelihood probability of test samples, in which each category adaptively selected the most matching dictionary and mapping matrix for high-resolution reconstruction, finally, the non-local similarity and iterative back-projection are exploited to furtherly improve the quality of the reconstruction image.The experimental results demonstrate the validity of the proposed algorithm.
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2016, Vol. 44 
