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- Publisher Website: 10.1109/ICCV48922.2021.00407
- WOS: WOS:000797698904030
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Conference Paper: Star: A structure-aware lightweight transformer for real-time image enhancement
Title | Star: A structure-aware lightweight transformer for real-time image enhancement |
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Authors | |
Keywords | Image quality Adaptation models Image color analysis Lighting Streaming media |
Issue Date | 2021 |
Publisher | IEEE Computer Society. |
Citation | 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 10-17 October 2021. In Proceedings: 2021 IEEE/CVF International Conference on Computer Vision: ICCV 2021, p. 4106-4115 How to Cite? |
Abstract | Image and video enhancement such as color constancy, low light enhancement, and tone mapping on smartphones is challenging, because high-quality images should be achieved efficiently with a limited resource budget. Unlike prior works that either used very deep CNNs or large Trans-former models, we propose a structure-aware lightweight Transformer, termed STAR, for real-time image enhancement. STAR is formulated to capture long-range dependencies between image patches, which naturally and implicitly captures the structural relationships of different regions in an image. STAR is a general architecture that can be easily adapted to different image enhancement tasks. Extensive experiments show that STAR can effectively boost the quality and efficiency of many tasks such as illumination enhancement, auto white balance, and photo retouching, which are indispensable components for image processing on smartphones. For example, STAR reduces model complexity and improves image quality compared to the recent state-of-the-art [19] on the MIT-Adobe FiveK dataset [7] (i.e., 1.8dB PSNR improvements with 25% parameters and 13% float operations.) |
Persistent Identifier | http://hdl.handle.net/10722/315802 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Z | - |
dc.contributor.author | Jiang, Y | - |
dc.contributor.author | Jiang, J | - |
dc.contributor.author | Wang, X | - |
dc.contributor.author | Luo, P | - |
dc.contributor.author | Gu, J | - |
dc.date.accessioned | 2022-08-19T09:04:42Z | - |
dc.date.available | 2022-08-19T09:04:42Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 10-17 October 2021. In Proceedings: 2021 IEEE/CVF International Conference on Computer Vision: ICCV 2021, p. 4106-4115 | - |
dc.identifier.uri | http://hdl.handle.net/10722/315802 | - |
dc.description.abstract | Image and video enhancement such as color constancy, low light enhancement, and tone mapping on smartphones is challenging, because high-quality images should be achieved efficiently with a limited resource budget. Unlike prior works that either used very deep CNNs or large Trans-former models, we propose a structure-aware lightweight Transformer, termed STAR, for real-time image enhancement. STAR is formulated to capture long-range dependencies between image patches, which naturally and implicitly captures the structural relationships of different regions in an image. STAR is a general architecture that can be easily adapted to different image enhancement tasks. Extensive experiments show that STAR can effectively boost the quality and efficiency of many tasks such as illumination enhancement, auto white balance, and photo retouching, which are indispensable components for image processing on smartphones. For example, STAR reduces model complexity and improves image quality compared to the recent state-of-the-art [19] on the MIT-Adobe FiveK dataset [7] (i.e., 1.8dB PSNR improvements with 25% parameters and 13% float operations.) | - |
dc.language | eng | - |
dc.publisher | IEEE Computer Society. | - |
dc.relation.ispartof | Proceedings: 2021 IEEE/CVF International Conference on Computer Vision: ICCV 2021 | - |
dc.rights | Proceedings: 2021 IEEE/CVF International Conference on Computer Vision: ICCV 2021. Copyright © IEEE Computer Society. | - |
dc.subject | Image quality | - |
dc.subject | Adaptation models | - |
dc.subject | Image color analysis | - |
dc.subject | Lighting | - |
dc.subject | Streaming media | - |
dc.title | Star: A structure-aware lightweight transformer for real-time image enhancement | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Luo, P: pluo@hku.hk | - |
dc.identifier.authority | Luo, P=rp02575 | - |
dc.identifier.doi | 10.1109/ICCV48922.2021.00407 | - |
dc.identifier.hkuros | 335604 | - |
dc.identifier.spage | 4106 | - |
dc.identifier.epage | 4115 | - |
dc.identifier.isi | WOS:000797698904030 | - |
dc.publisher.place | United States | - |