Single-image super-resolution: A benchmark

Chih Yuan Yang; Chao Ma; Ming Hsuan Yang

doi:10.1007/978-3-319-10593-2_25

Single-image super-resolution: A benchmark

Chih Yuan Yang, Chao Ma, Ming Hsuan Yang

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

427 Citations (Scopus)

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 language	English
Title of host publication	Computer Vision, ECCV 2014 - 13th European Conference, Proceedings
Publisher	Springer Verlag
Pages	372-386
Number of pages	15
Edition	PART 4
ISBN (Print)	9783319105925
DOIs	https://doi.org/10.1007/978-3-319-10593-2_25
Publication status	Published - 2014
Event	13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland Duration: 2014 Sept 6 → 2014 Sept 12

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 4
Volume	8692 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th European Conference on Computer Vision, ECCV 2014
Country/Territory	Switzerland
City	Zurich
Period	14/9/6 → 14/9/12

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-319-10593-2_25

Cite this

Yang, C. Y., Ma, C., & Yang, M. H. (2014). Single-image super-resolution: A benchmark. In Computer Vision, ECCV 2014 - 13th European Conference, Proceedings (PART 4 ed., pp. 372-386). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8692 LNCS, No. PART 4). Springer Verlag. https://doi.org/10.1007/978-3-319-10593-2_25

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title = "Single-image super-resolution: A benchmark",

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.",

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booktitle = "Computer Vision, ECCV 2014 - 13th European Conference, Proceedings",

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Yang, CY, Ma, C & Yang, MH 2014, Single-image super-resolution: A benchmark. in Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 4 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 4, vol. 8692 LNCS, Springer Verlag, pp. 372-386, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 14/9/6. https://doi.org/10.1007/978-3-319-10593-2_25

Single-image super-resolution: A benchmark. / Yang, Chih Yuan; Ma, Chao; Yang, Ming Hsuan.
Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 4. ed. Springer Verlag, 2014. p. 372-386 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8692 LNCS, No. PART 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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Single-image super-resolution: A benchmark

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