ARetinex-Net: Low-light image enhancement with adaptive retinex model and global illumination representation.

Abstract

Low-light image enhancement (LLIE) aims to improve the visibility and perceptual quality of images captured under insufficient illumination. Recent VQ-based LLIE methods introduce data-driven priors by quantizing normal-light latent features into a vector-quantized (VQ) codebook. However, directly quantizing rich entangled representations often causes severe information loss and requires additional fusion with degraded low-light features, increasing complexity and limiting restoration quality. To address this issue, we build on Retinex theory and propose an Adaptive Retinex Model (ARM) that decomposes an input image into structural (reflectance) and illumination components without relying on handcrafted priors or regularization terms. On top of the illumination branch, we introduce a Global Illumination Representation (GIR) module that learns a VQ codebook of global illumination patterns from normal-light images and uses it as an explicit prior for enhancement. To effectively exploit this illumination codebook for low-light inputs, we design an Illumination Prior Query (IPQ) module and a single-objective query loss, improving the stability and adaptivity of retrieval under the low-/normal-light distribution gap. The resulting framework, termed Adaptive Retinex Network (ARetinex-Net), is extensively evaluated on both synthetic and real-world benchmarks, where it consistently outperforms state-of-the-art LLIE methods in terms of quantitative metrics and visual quality.