An Adaptive Image Dehazing Network with Multi-Color Feature for Complex Real-World Hazy Scenes

Abstract

Real-world hazy scenes can be broadly categorized into four types based on haze distribution and concentration: light homogeneous haze, dense homogeneous haze, light non-homogeneous haze, and dense non-homogeneous haze. However, many existing dehazing models are tailored for specific haze types, struggling to generalize effectively across these diverse conditions. Additionally, these models commonly extract feature information in the Red, Green, and Blue (RGB) color space, which makes it challenging to extract sufficient feature information in various hazy scenes. To address this issue, we propose an Adaptive Network (AdaNet) for multiple hazy scenes. The network includes two sub-networks: a color-guided feature extraction network and a scene reconstruction network. The color-guided feature extraction network is used to capture sufficient color, detail, and other feature information in both RGB and Luminance, Chroma Red, Chroma Blue (YCrCb) color spaces. For light and dense non-homogeneous hazy scenes, we enhance the scene reconstruction network with the Feature Selection Units (FSU) to filter out less relevant information, ensuring precise recovery of critical local details. Additionally, to tackle dehazing in light and dense homogeneous hazy scenes, we integrate the Feature Fusion Units (FFU) that combine multi-level features to improve overall feature utilization. Extensive experiments on multiple datasets with diverse hazy scenes demonstrate that our AdaNet outperforms state-of-the-art dehazing models, producing high-quality dehazed images in quadruple haze scenarios and ensuring reliability for high-level visual tasks in real-world hazy scenes.