Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908438555000832 |
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| author | Xu, Xiaogang Zhou, Kun Hu, Tao Wu, Jiafei Wang, Ruixing Peng, Hao Yu, Bei |
| author_facet | Xu, Xiaogang Zhou, Kun Hu, Tao Wu, Jiafei Wang, Ruixing Peng, Hao Yu, Bei |
| contents | Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise. In this paper, we present an innovative video decomposition strategy that incorporates view-independent and view-dependent components to enhance the performance of LLVE. We leverage dynamic cross-frame correspondences for the view-independent term (which primarily captures intrinsic appearance) and impose a scene-level continuity constraint on the view-dependent term (which mainly describes the shading condition) to achieve consistent and satisfactory decomposition results. To further ensure consistent decomposition, we introduce a dual-structure enhancement network featuring a cross-frame interaction mechanism. By supervising different frames simultaneously, this network encourages them to exhibit matching decomposition features. This mechanism can seamlessly integrate with encoder-decoder single-frame networks, incurring minimal additional parameter costs. Extensive experiments are conducted on widely recognized LLVE benchmarks, covering diverse scenarios. Our framework consistently outperforms existing methods, establishing a new SOTA performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15660 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition Xu, Xiaogang Zhou, Kun Hu, Tao Wu, Jiafei Wang, Ruixing Peng, Hao Yu, Bei Computer Vision and Pattern Recognition Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise. In this paper, we present an innovative video decomposition strategy that incorporates view-independent and view-dependent components to enhance the performance of LLVE. We leverage dynamic cross-frame correspondences for the view-independent term (which primarily captures intrinsic appearance) and impose a scene-level continuity constraint on the view-dependent term (which mainly describes the shading condition) to achieve consistent and satisfactory decomposition results. To further ensure consistent decomposition, we introduce a dual-structure enhancement network featuring a cross-frame interaction mechanism. By supervising different frames simultaneously, this network encourages them to exhibit matching decomposition features. This mechanism can seamlessly integrate with encoder-decoder single-frame networks, incurring minimal additional parameter costs. Extensive experiments are conducted on widely recognized LLVE benchmarks, covering diverse scenarios. Our framework consistently outperforms existing methods, establishing a new SOTA performance. |
| title | Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.15660 |