Low-light Image Enhancement with Retinex Decomposition in Latent Space

2026-03-16

AI Summary

This work addresses the challenge of accurately decoupling reflectance and illumination components in low-light image enhancement. To this end, the authors propose a two-stage Retinex-guided Transformer model. In the first stage, the multiplicative Retinex model is transformed into an additive form in latent space through a logarithmic transformation with a one-pixel offset, enabling stable decomposition. The second stage employs a U-shaped refinement module that integrates Transformer-based attention mechanisms to enhance detail recovery and refine illumination distribution. By innovatively combining latent-space decomposition with a guided Transformer architecture, the method achieves competitive enhancement performance across four benchmark datasets while significantly improving training stability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance

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.

Problem

• Low-light image enhancement
• Retinex decomposition
• Reflectance and illumination separation
• Image decomposition
• Latent space

Innovation

• Retinex decomposition
• latent space
• Transformer
• low-light enhancement
• additive formulation