FADR-Net: fog-aware deformable and Retinex-guided network for image dehazing
Xiaohan Guan, Shiyuan Zhou, Daming Lin, Shumao Qiu · The Imaging Science Journal · 2026
Haze degrades image quality and undermines the performance of vision systems in detection and recognition tasks, especially in complex scenes. Although lightweight methods such as AOD-Net are computationally efficient, they exhibit two critical limitations: limited adaptability to spatially varying haze and insufficient preservation of fine image details. To overcome these issues, we propose an enhanced AOD-Net-based dehazing algorithm. Specifically, we introduce a Fog-Aware Dynamically Constrained Deformable Convolution to better handle spatially varying haze. Inspired by Retinex theory, we further design a lightweight Detailed Enhancement Module that decomposes an image into illumination and reflectance components, enabling separate enhancement of global brightness and local details. In addition, we employ a composite loss that combines MS-SSIM and L1 losses to better preserve structural information during training. Experiments on the RESIDE benchmark show that our method achieves higher PSNR and SSIM than AOD-Net, while producing dehazed images with improved naturalness and visual quality.