AI RESEARCH
Low-light Image Enhancement with Retinex Decomposition in Latent Space
arXiv CS.CV
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ArXi:2603.15131v1 Announce Type: new 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.