用潜空间分解提升暗光图像增强效果，更稳定更清晰
Low-light Image Enhancement with Retinex Decomposition in Latent Space
- 在潜空间通过对数变换实现光照与反射的加法分离
- 在四个数据集上达到领先性能，训练过程更稳定
- 适合需要细节保留和光照优化的图像增强任务
Retinex理论为暗光图像增强提供了理论基础，启发了诸多基于学习的方法。然而现有方法在准确分解反射率与光照成分方面存在局限。为此，我们提出一种两阶段的Retinex引导变压器模型（RGT）：首先在潜空间采用对数变换与1像素偏移，将原本乘性关系转为加性形式，提升分解稳定性与精度；随后设计一个U型组件精修模块，结合提出的引导融合变压器块，优化反射成分以保留纹理细节并改善光照分布，有效将暗光输入转换为正常光照图像。在四个基准数据集上的实验验证表明，该方法在暗光增强任务中表现优异，且训练过程更稳定。
原文摘要 · Abstract (English)
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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