Unsupervised Super-Resolution of Magnetic Resonance Images Using Deep Image Prior

The anatomical resolution of MRI is typically limited by acquisition time constraints. While deep learning networks have shown great potential for post-acquisition MRI resolution enhancement, their training typically relies on low-high resolution image pairs, which are not always available in practice. Here, we propose using deep image prior (DIP) for unsupervised MRI resolution enhancement with network training relying only on low-resolution images. Experimental results indicate that our method super-resolve MR images effectively with realistic details.