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.