DARF-Net: A Dual Attention Retinex-Based Fusion Network for Low-Light Image Enhancement

Low-light images often suffer from poor contrast, reduced visibility, and detail loss, with artifacts and distortions under complex lighting further complicating enhancement. To address these challenges, we introduce DARF-Net, a Retinex-inspired framework that separates input images into illumination and reflectance components for targeted enhancement. Our method, which uses an Illumination-Guided Multi-Head Attention module as the generator of a generative adversarial network, improves the illumination map, while a Variational Autoencoder supplemented with a Spatial Attention Module improves the reflectance map. Together, the two attention modules improve brightness, structure, and overall visual quality. Comprehensive tests on the LOL and SICE datasets show that DARF-Net outperforms the state of-the-art techniques in terms of PSNR, SSIM, and LPIPS metrics while retaining computational efficiency. The code is available at: https://github.com/SampritBose/DARF-Net