Seeing Through Dense Fog With an Intelligent Adaptive Dehazing System

Most existing image dehazing methods are mainly suitable for synthetic datasets and often perform poorly in real-world, complex hazy scenarios. To address this issue, this paper proposes an intelligent adaptive dehazing system that integrates range-gated imaging with deep learning, combining image acquisition and restoration for enhanced dehazing performance. The range-gated imaging system reduces scattered light interference. However, our approach primarily focuses on enhancing image quality through advanced dehazing methods. Specifically, we introduce the MSCENAFormer dehazing network, which achieves high-quality reconstruction of target scenes in dense fog by effectively removing fog and improving visibility. The core modules of MSCENAFormer include the multi-scale enhanced neighborhood attention module (MSENA) and the comprehensive attention refinement module (CARM). MSENA is designed to capture rich local information in harsh environment, improving the dehazing effect and enhancing image details. CARM integrates the local and global information to optimize the visual effect further. In addition, the adaptive feature mixing module (AFM) is used to fuse multi-scale features for better performance. To validate the performance of our method, we utilize our lab-collected non-homogeneous haze real dataset, O-ITDF, along with the public datasets NH-HAZE, NTIRE2021 and NTIRE2023. Experimental results demonstrate that our proposed MSCENAFormer outperforms many methods. We share our code at https://github.com/NingCao-zzu/MSCENAFormer.