Non-local kernel regression for image and video restoration

Haichao Zhang, Jianchao Yang, Yanning Zhang, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

92 Scopus citations

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.

Original languageEnglish
Title of host publicationComputer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings
PublisherSpringer Verlag
Pages566-579
Number of pages14
EditionPART 3
ISBN (Print)364215557X, 9783642155574
DOIs
StatePublished - 2010
Event11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece
Duration: 10 Sep 201011 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6313 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th European Conference on Computer Vision, ECCV 2010
Country/TerritoryGreece
CityHeraklion, Crete
Period10/09/1011/09/10

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