Local multi-scale feature aggregation network for real-time image dehazing

Haze causes visual degradation and obscures image information, which gravely affects the reliability of computer vision tasks in real-time systems. Leveraging an enormous number of learning parameters as the restoration costs, learning-based methods have gained significant success, but they are runtime intensive or memory inefficient. In this paper, we propose a local multi-scale feature aggregation network, called LMFA-Net, which has a lightweight model structure and can be used for real-time dehazing. By learning the local mapping relationship between the clean value of a haze image at a certain point and its surrounding local region, LMFA-Net can directly restore the final haze-free image. In particular, we adopt a novel multi-scale feature extraction sub-network (M-Net) to extract features from different scales. As a lightweight network, LMFA-Net can achieve fast and efficient dehazing. Extensive experiments demonstrate that our proposed LMFA-Net surpasses previous state-of-the-art lightweight dehazing methods in both quantitatively and qualitatively.