A Real-time Image Dehazing Network Fusing Multi-scale and Local Feature Enhancement

Image dehazing is a classic and challenging task in the field of computer vision, which is often applied in automatic driving, outdoor monitoring, and other scenarios susceptible to environmental interference, and is often used as a pre-processing manner to ensure the normal play of the efficacy of the downstream vision systems. To realize real-time processing of hazy images, an image dehazing algorithm fusing multi-scale and local feature enhancement is proposed in this work. Firstly, a lightweight feature extraction module is designed to reduce the network computation while extracting local features to improve the model operation efficiency; Additionally, considering the semantic information contained in different scale features, which can help the model to obtain more discriminative feature representations, and then accurately locate the extent of the haze area and process it in a targeted way, a multi-scale fusion module is proposed; Finally, an enhanced decoding strategy is introduced to fuse the upper layer decoding features with the multi-scale features so that the network can adaptively focus on the haze region and boost the dehazing performance of the model. Experiments on synthetic and real-world datasets demonstrate that the proposed algorithm excels in both dehazing performance and inference speed compared to existing mainstream image dehazing methods, with PSNR and SSIM metrics of 32.32 dB/0.979 and 33.39 dB/0.982 on SOTS indoor and outdoor datasets, respectively.