COUPLED DICTIONARY LEARNING ON COMMON FEATURE SPACE FOR MEDICAL IMAGE SUPER RESOLUTION

Resolution in medical images is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. In this paper, we propose a coupled dictionary learning approach for super resolution of medical images, in which canonical correlation analysis (CCA) is applied to construct a common feature space. Then a pair of coupled dictionaries are learned on the derived space. At last, we seek a sparse representation for each patch of the low-resolution input, and use the sparse coefficients to generate the high-resolution output. The experimental results show that the proposed method is competitive or even superior to the other state-of-the-art SR methods.