Single-image super-resolution: A benchmark

Chih Yuan Yang, Chao Ma, Ming Hsuan Yang

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

477 Scopus citations

Abstract

Single-image super-resolution is of great importance for vision applications, and numerous algorithms have been proposed in recent years. Despite the demonstrated success, these results are often generated based on different assumptions using different datasets and metrics. In this paper, we present a systematic benchmark evaluation for state-of-the-art single-image super-resolution algorithms. In addition to quantitative evaluations based on conventional full-reference metrics, human subject studies are carried out to evaluate image quality based on visual perception. The benchmark evaluations demonstrate the performance and limitations of state-of-the-art algorithms which sheds light on future research in single-image super-resolution.

Original languageEnglish
Title of host publicationComputer Vision, ECCV 2014 - 13th European Conference, Proceedings
PublisherSpringer Verlag
Pages372-386
Number of pages15
EditionPART 4
ISBN (Print)9783319105925
DOIs
StatePublished - 2014
Externally publishedYes
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: 06 09 201412 09 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume8692 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th European Conference on Computer Vision, ECCV 2014
Country/TerritorySwitzerland
CityZurich
Period06/09/1412/09/14

Keywords

  • Gaussian blur kernel width
  • Single-image super-resolution
  • metrics
  • performance evaluation

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