Low-light Image Enhancement with Retinex Decomposition in Latent Space
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910054595166208 |
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| author | Zheng, Bolun Lei, Qingshan Chen, Quan Zhang, Qianyu Yu, Kainan Jia, Xu Zhu, Lingyu |
| author_facet | Zheng, Bolun Lei, Qingshan Chen, Quan Zhang, Qianyu Yu, Kainan Jia, Xu Zhu, Lingyu |
| contents | Retinex theory provides a principled foundation for low-light image enhancement, inspiring numerous learning-based methods that integrate its principles. However, existing methods exhibits limitations in accurately decomposing reflectance and illumination components. To address this, we propose a Retinex-Guided Transformer~(RGT) model, which is a two-stage model consisting of decomposition and enhancement phases. First, we propose a latent space decomposition strategy to separate reflectance and illumination components. By incorporating the log transformation and 1-pixel offset, we convert the intrinsically multiplicative relationship into an additive formulation, enhancing decomposition stability and precision. Subsequently, we construct a U-shaped component refiner incorporating the proposed guidance fusion transformer block. The component refiner refines reflectance component to preserve texture details and optimize illumination distribution, effectively transforming low-light inputs to normal-light counterparts. Experimental evaluations across four benchmark datasets validate that our method achieves competitive performance in low-light enhancement and a more stable training process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15131 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Low-light Image Enhancement with Retinex Decomposition in Latent Space Zheng, Bolun Lei, Qingshan Chen, Quan Zhang, Qianyu Yu, Kainan Jia, Xu Zhu, Lingyu Computer Vision and Pattern Recognition Retinex theory provides a principled foundation for low-light image enhancement, inspiring numerous learning-based methods that integrate its principles. However, existing methods exhibits limitations in accurately decomposing reflectance and illumination components. To address this, we propose a Retinex-Guided Transformer~(RGT) model, which is a two-stage model consisting of decomposition and enhancement phases. First, we propose a latent space decomposition strategy to separate reflectance and illumination components. By incorporating the log transformation and 1-pixel offset, we convert the intrinsically multiplicative relationship into an additive formulation, enhancing decomposition stability and precision. Subsequently, we construct a U-shaped component refiner incorporating the proposed guidance fusion transformer block. The component refiner refines reflectance component to preserve texture details and optimize illumination distribution, effectively transforming low-light inputs to normal-light counterparts. Experimental evaluations across four benchmark datasets validate that our method achieves competitive performance in low-light enhancement and a more stable training process. |
| title | Low-light Image Enhancement with Retinex Decomposition in Latent Space |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.15131 |