Single Image Super Resolution - When Model Adaptation Matters Article Swipe
Yudong Liang
,
Radu Timofte
,
Jinjun Wang
,
Yihong Gong
,
Nanning Zheng
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1703.10889
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1703.10889
In the recent years impressive advances were made for single image super-resolution. Deep learning is behind a big part of this success. Deep(er) architecture design and external priors modeling are the key ingredients. The internal contents of the low resolution input image is neglected with deep modeling despite the earlier works showing the power of using such internal priors. In this paper we propose a novel deep convolutional neural network carefully designed for robustness and efficiency at both learning and testing. Moreover, we propose a couple of model adaptation strategies to the internal contents of the low resolution input image and analyze their strong points and weaknesses. By trading runtime and using internal priors we achieve 0.1 up to 0.3dB PSNR improvements over best reported results on standard datasets. Our adaptation especially favors images with repetitive structures or under large resolutions. Moreover, it can be combined with other simple techniques, such as back-projection or enhanced prediction, for further improvements.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1703.10889
- https://arxiv.org/pdf/1703.10889
- OA Status
- green
- Cited By
- 11
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2605134680
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https://openalex.org/W2605134680Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.1703.10889Digital Object Identifier
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Single Image Super Resolution - When Model Adaptation MattersWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2017Year of publication
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2017-03-31Full publication date if available
- Authors
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Yudong Liang, Radu Timofte, Jinjun Wang, Yihong Gong, Nanning ZhengList of authors in order
- Landing page
-
https://arxiv.org/abs/1703.10889Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1703.10889Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1703.10889Direct OA link when available
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Prior probability, Computer science, Robustness (evolution), Convolutional neural network, Deep learning, Artificial intelligence, Image (mathematics), Adaptation (eye), Pattern recognition (psychology), Machine learning, Gene, Biochemistry, Optics, Chemistry, Physics, Bayesian probabilityTop concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
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2021: 1, 2020: 3, 2019: 1, 2018: 3, 2017: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.combined | 145 |
| abstract_inverted_index.contents | 35, 93 |
| abstract_inverted_index.designed | 71 |
| abstract_inverted_index.enhanced | 154 |
| abstract_inverted_index.external | 26 |
| abstract_inverted_index.internal | 34, 57, 92, 112 |
| abstract_inverted_index.learning | 13, 78 |
| abstract_inverted_index.modeling | 28, 46 |
| abstract_inverted_index.reported | 124 |
| abstract_inverted_index.standard | 127 |
| abstract_inverted_index.success. | 21 |
| abstract_inverted_index.testing. | 80 |
| abstract_inverted_index.Moreover, | 81, 141 |
| abstract_inverted_index.carefully | 70 |
| abstract_inverted_index.datasets. | 128 |
| abstract_inverted_index.neglected | 43 |
| abstract_inverted_index.adaptation | 88, 130 |
| abstract_inverted_index.efficiency | 75 |
| abstract_inverted_index.especially | 131 |
| abstract_inverted_index.impressive | 4 |
| abstract_inverted_index.repetitive | 135 |
| abstract_inverted_index.resolution | 39, 97 |
| abstract_inverted_index.robustness | 73 |
| abstract_inverted_index.strategies | 89 |
| abstract_inverted_index.structures | 136 |
| abstract_inverted_index.prediction, | 155 |
| abstract_inverted_index.techniques, | 149 |
| abstract_inverted_index.weaknesses. | 106 |
| abstract_inverted_index.architecture | 23 |
| abstract_inverted_index.improvements | 121 |
| abstract_inverted_index.ingredients. | 32 |
| abstract_inverted_index.resolutions. | 140 |
| abstract_inverted_index.convolutional | 67 |
| abstract_inverted_index.improvements. | 158 |
| abstract_inverted_index.back-projection | 152 |
| abstract_inverted_index.super-resolution. | 11 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |