Rethinking Low-Light Image Enhancement: A Log-Domain Intensity--Chromaticity Decoupling Perspective
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866917458709839872 |
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| author | Bai, Guangrui Mei, Yifan Deng, Yahui Chen, Yuhan Qiu, Yuze Liu, Wenhai Dong, Erbao |
| author_facet | Bai, Guangrui Mei, Yifan Deng, Yahui Chen, Yuhan Qiu, Yuze Liu, Wenhai Dong, Erbao |
| contents | Explicit reconstruction constraints derived from the decoupled representation are further imposed to suppress abnormal channel amplification and chromatic noise. Experiments on LOLv2-Real, MIT-Adobe FiveK, and LSRW show that the proposed method achieves competitive or superior quantitative and visual performance, reaching 29.71 dB PSNR and 0.89 SSIM on LOLv2-Real. DarkFace experiments further indicate improved downstream face detection under low-light conditions. Code and pretrained models are available at: https://github.com/mubaisam/ICD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02627 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Rethinking Low-Light Image Enhancement: A Log-Domain Intensity--Chromaticity Decoupling Perspective Bai, Guangrui Mei, Yifan Deng, Yahui Chen, Yuhan Qiu, Yuze Liu, Wenhai Dong, Erbao Computer Vision and Pattern Recognition Explicit reconstruction constraints derived from the decoupled representation are further imposed to suppress abnormal channel amplification and chromatic noise. Experiments on LOLv2-Real, MIT-Adobe FiveK, and LSRW show that the proposed method achieves competitive or superior quantitative and visual performance, reaching 29.71 dB PSNR and 0.89 SSIM on LOLv2-Real. DarkFace experiments further indicate improved downstream face detection under low-light conditions. Code and pretrained models are available at: https://github.com/mubaisam/ICD. |
| title | Rethinking Low-Light Image Enhancement: A Log-Domain Intensity--Chromaticity Decoupling Perspective |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.02627 |