Abstract

This paper presents a non-local kernel regression (NL-KR) method for image and video restoration tasks, which exploits both the non-local self-similarity and local structural regularity in natural im- ages. The non-local self-similarity is based on the observation that image patches tend to repeat themselves in natural images and videos; and the local structural regularity reveals that image patches have regular struc- tures where accurate estimation of pixel values via regression is possible. Explicitly unifying both properties, the proposed non-local kernel re- gression framework is robust and applicable to various image and video restoration tasks. In this work, we are specifically interested in applying the NL-KR model to image and video super-resolution (SR) reconstruc- tion. Extensive experimental results on both single images and realistic video sequences demonstrate the superiority of the proposed framework for SR tasks over previous works both qualitatively and quantitatively.