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|a 10.1109/TIP.2023.3301332
|2 doi
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|a pubmed24n1201.xml
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|a eng
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| 100 |
1 |
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|a Lv, Xiaoqian
|e verfasserin
|4 aut
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| 245 |
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|a Unsupervised Low-Light Video Enhancement With Spatial-Temporal Co-Attention Transformer
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 16.08.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Existing low-light video enhancement methods are dominated by Convolution Neural Networks (CNNs) that are trained in a supervised manner. Due to the difficulty of collecting paired dynamic low/normal-light videos in real-world scenes, they are usually trained on synthetic, static, and uniform motion videos, which undermines their generalization to real-world scenes. Additionally, these methods typically suffer from temporal inconsistency (e.g., flickering artifacts and motion blurs) when handling large-scale motions since the local perception property of CNNs limits them to model long-range dependencies in both spatial and temporal domains. To address these problems, we propose the first unsupervised method for low-light video enhancement to our best knowledge, named LightenFormer, which models long-range intra- and inter-frame dependencies with a spatial-temporal co-attention transformer to enhance brightness while maintaining temporal consistency. Specifically, an effective but lightweight S-curve Estimation Network (SCENet) is first proposed to estimate pixel-wise S-shaped non-linear curves (S-curves) to adaptively adjust the dynamic range of an input video. Next, to model the temporal consistency of the video, we present a Spatial-Temporal Refinement Network (STRNet) to refine the enhanced video. The core module of STRNet is a novel Spatial-Temporal Co-attention Transformer (STCAT), which exploits multi-scale self- and cross-attention interactions to capture long-range correlations in both spatial and temporal domains among frames for implicit motion estimation. To achieve unsupervised training, we further propose two non-reference loss functions based on the invertibility of the S-curve and the noise independence among frames. Extensive experiments on the SDSD and LLIV-Phone datasets demonstrate that our LightenFormer outperforms state-of-the-art methods
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|a Journal Article
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|a Zhang, Shengping
|e verfasserin
|4 aut
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|a Wang, Chenyang
|e verfasserin
|4 aut
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| 700 |
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|a Zhang, Weigang
|e verfasserin
|4 aut
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| 700 |
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|a Yao, Hongxun
|e verfasserin
|4 aut
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| 700 |
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|a Huang, Qingming
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 32(2023) vom: 07., Seite 4701-4715
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|x 1941-0042
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|g volume:32
|g year:2023
|g day:07
|g pages:4701-4715
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|u http://dx.doi.org/10.1109/TIP.2023.3301332
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