Low-light Image Enhancement with Retinex Decomposition in Latent Space

• URL: http://arxiv.org/abs/2603.15131v1
• Date: Mon, 16 Mar 2026 11:24:35 GMT
• Title: Low-light Image Enhancement with Retinex Decomposition in Latent Space
• Abstract summary: We propose a Retinex-Guided Transformer(RGT) model, which is a two-stage model consisting of decomposition and enhancement phases.<n>By incorporating the log transformation and 1-pixel offset, we convert the intrinsically multiplicative relationship into an additive formulation.<n>The component refiner refines reflectance component to preserve texture details and optimize illumination distribution, effectively transforming low-light inputs to normal-light counterparts.
• Score: 17.678509073626284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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