Exploring Fast and Flexible Zero‐Shot Low‐Light Image/Video Enhancement
Low‐light image/video enhancement is a challenging task when images or video are captured under harsh lighting conditions. Existing methods mostly formulate this task as an image‐to‐image conversion task via supervised or unsupervised learning. However, such conversion methods require an extremely l...
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| Vydané v: | Computer graphics forum Ročník 43; číslo 7 |
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| Jazyk: | English |
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Blackwell Publishing Ltd
01.10.2024
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Low‐light image/video enhancement is a challenging task when images or video are captured under harsh lighting conditions. Existing methods mostly formulate this task as an image‐to‐image conversion task via supervised or unsupervised learning. However, such conversion methods require an extremely large amount of data for training, whether paired or unpaired. In addition, these methods are restricted to specific training data, making it difficult for the trained model to enhance other types of images or video. In this paper, we explore a novel, fast and flexible, zero‐shot, low‐light image or video enhancement framework. Without relying on prior training or relationships among neighboring frames, we are committed to estimating the illumination of the input image/frame by a well‐designed network. The proposed zero‐shot, low‐light image/video enhancement architecture includes illumination estimation and residual correction modules. The network architecture is very concise and does not require any paired or unpaired data during training, which allows low‐light enhancement to be performed with several simple iterations. Despite its simplicity, we show that the method is fast and generalizes well to diverse lighting conditions. Many experiments on various images and videos qualitatively and quantitatively demonstrate the advantages of our method over state‐of‐the‐art methods. |
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| AbstractList | Low‐light image/video enhancement is a challenging task when images or video are captured under harsh lighting conditions. Existing methods mostly formulate this task as an image‐to‐image conversion task via supervised or unsupervised learning. However, such conversion methods require an extremely large amount of data for training, whether paired or unpaired. In addition, these methods are restricted to specific training data, making it difficult for the trained model to enhance other types of images or video. In this paper, we explore a novel, fast and flexible, zero‐shot, low‐light image or video enhancement framework. Without relying on prior training or relationships among neighboring frames, we are committed to estimating the illumination of the input image/frame by a well‐designed network. The proposed zero‐shot, low‐light image/video enhancement architecture includes illumination estimation and residual correction modules. The network architecture is very concise and does not require any paired or unpaired data during training, which allows low‐light enhancement to be performed with several simple iterations. Despite its simplicity, we show that the method is fast and generalizes well to diverse lighting conditions. Many experiments on various images and videos qualitatively and quantitatively demonstrate the advantages of our method over state‐of‐the‐art methods. |
| Author | Yang, Hongyu Bao, Taoli Han, Xianjun |
| Author_xml | – sequence: 1 givenname: Xianjun orcidid: 0000-0001-7674-1428 surname: Han fullname: Han, Xianjun email: hxj@ahu.edu.cn organization: Anhui University – sequence: 2 givenname: Taoli surname: Bao fullname: Bao, Taoli organization: Anhui University – sequence: 3 givenname: Hongyu surname: Yang fullname: Yang, Hongyu organization: Sichuan University |
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| Cites_doi | 10.1109/CVPR52688.2022.01710 10.1007/s11263-018-01144-2 10.24963/ijcai.2022/128 10.1016/j.patcog.2016.06.008 10.1109/WACVW54805.2022.00064 10.1117/1.1636183 10.1109/TIP.2018.2810539 10.1109/TIP.2013.2261309 10.1109/FG.2018.00118 10.1145/954339.954342 10.1109/LSP.2012.2227726 10.1109/TCSVT.2021.3073371 10.1109/ICCV48922.2021.00956 10.1016/S0734-189X(87)80186-X 10.1609/aaai.v36i2.20046 10.1109/CVPR.2016.304 10.1109/ICPR.2010.579 10.1109/TIP.2018.2794218 10.1007/s11263-020-01407-x 10.1109/83.597272 10.1109/ICASSP43922.2022.9746255 10.1109/CVPR.2019.00387 10.1109/83.557356 10.1109/CVPR.2018.00347 10.1109/TIP.2015.2474701 10.1016/j.patcog.2021.108234 10.1109/CVPRW.2019.00247 10.1109/TIP.2021.3051462 10.1109/TBC.2008.2000733 10.1145/3272127.3275081 |
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| Copyright | 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd. 2024 The Eurographics Association and John Wiley & Sons Ltd. |
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| SubjectTerms | CCS Concepts Computational photography Computing methodologies → Image processing Estimation Illumination Image enhancement Light Lighting Unsupervised learning Video data |
| Title | Exploring Fast and Flexible Zero‐Shot Low‐Light Image/Video Enhancement |
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