Self-synthesis based super-resolution for a natural image

In this paper, we propose self-synthesis based super-resolution algorithm for natural images. The proposed method analyzes the property of the natural image and focuses on the self-similarity of natural objects. Structure and texture components are extracted from the self-image and synthesized on the interpolated image with matching weights to restore the self-similarity of interpolated image. Then, the synthesized image is compensated with MAP estimator to prevent artifacts. Experimental results show that the proposed method gives more natural texture in terms of human perception.