Non-local kernel regression for image and video restoration

Authors:
Haichao Zhang;Jianchao Yang;Yanning Zhang;Thomas S. Huang
Affiliations:
School of Computer Science, Northwestern Polytechnical University, Xi'an, China and Beckman Institute, University of Illinois at Urbana-Champaign;Beckman Institute, University of Illinois at Urbana-Champaign;School of Computer Science, Northwestern Polytechnical University, Xi'an, China;Beckman Institute, University of Illinois at Urbana-Champaign
Venue:
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Year:
2010

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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 images. 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 structures where accurate estimation of pixel values via regression is possible. Explicitly unifying both properties, the proposed non-local kernel regression 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) reconstruction. 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.