Structural similarity regularized and sparse coding based super-resolution for medical images

Recently the single image super-resolution reconstruction (SISR) via sparse coding has attracted increasing interests. Considering that there are obviously repetitive image structures in medical images, in this study we propose a regularized SISR method via sparse coding and structural similarity. The pixel based recovery is incorporated as a regularization term to exploit the non-local structural similarities of medical images, which is very helpful in further improving the quality of recovered medical images. An alternative variables optimization algorithm is proposed and some medical images including CT, MRI and ultrasound images are used to investigate the performance of our proposed method. The results show the superiority of our method to its counterparts.

► A method of structural similarity and sparse coding based super-resolution of medical images is proposed.