LWTD: a novel light-weight transformer-like CNN architecture for driving scene dehazing

With the rapid advancement of artificial intelligence and automation technology, interest in autonomous driving research is also growing. However, under heavy rain, fog, and other adverse weather conditions, the visual quality of the images is reduced due to suspended atmospheric particles that affect the vehicle’s visual perception system, which is not conducive to the autonomous driving system’s accurate perception of the road environment. To address these challenges, this article presents a computationally efficient end-to-end light-weight Transformer-like neural network called LWTD (Light-Weight Transformer-like DehazeNet) to reconstruct haze-free images for driving tasks, which based on the reformulated ASM theory without prior knowledge. First, a strategy for simplifying the atmospheric light and transmission map into a feature map is adopted, a CMT (Convolutional Mapping Transformer) module for the extraction of global features is developed, and the hazy image is decomposed into a base layer (global features) and a detail layer (local features) for Low-Level, Medium-Level, and High-Level stages. Meanwhile, a channel attention module is introduced to weigh and assign the weights of each feature, and to fuse them with the reformulated ASM (Atmospheric Scattering Model) model to restore the haze-free image. Second, a joint loss function of the graphical features is formulated to further direct the network to converge in the direction of abundant features. In addition, a dataset of real-world fog driving is constructed. Extensive experiments with synthetic and natural hazy images confirmed the superiority of the proposed method through quantitative and qualitative evaluations on various datasets. Furthermore, additional experiments validated the applicability of the proposed method for traffic participant detection and semantic segmentation tasks. The source code has been made publicly available on https://github.com/ZebGH/LWTD-Net.