Alpha-DehazeNet: single image dehazing via RGBA haze modeling and adaptive learning

Image dehazing is a vital research area in computer vision. Many existing deep learning-based dehazing methods rely on atmospheric scattering models with manually predefined, non-trainable parameters, which limits their adaptability and transferability. We propose Alpha-DehazeNet, a novel model that leverages red green blue alpha (RGBA) haze layer effect maps by defining a grayscale transparency map in the RGBA color space as the initial haze layer. Alpha-DehazeNet employs a U-Net generator enhanced with a spatial attention mechanism to encode haze-related features. This generator is integrated into an adversarial architecture with residual connections, enabling end-to-end training. Additionally, a depth consistency loss is introduced to improve dehazing accuracy. Alpha-DehazeNet outperforms several state-of-the-art models on synthetic datasets (ITS and OTS from RESIDE), achieving 37.35 dB peak signal-to-noise ratio (PSNR) on SOTS-indoor and 37.39 dB PSNR on SOTS-outdoor, while using only 8.86 million parameters. On real-world datasets, Alpha-DehazeNet delivers competitive results, although it shows limitations in handling non-white fog and cloud conditions. The code is publicly available at: https://doi.org/10.5281/zenodo.15361810.