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.
['Songze Tang', 'Haitao Guo', 'Nan Zhou', 'Lili Huang', 'Tianming Zhan']
Coupled dictionary learning on common feature space for medical image super resolution