Multi‐scale single image dehazing based on the fusion of global and local features
Ziyu Chen, Huaiyu Zhuang, Jia Han, Yani Cui, Jiaxian Deng · IET Image Processing · 2022
Abstract Aiming at the problems of incomplete dehazing of a single image and unnaturalness of the restored image, a multi‐scale single‐image defogging network with local features fused with global features is proposed, using fog and non‐fogging image pairs train the network in a direct end‐to‐end manner. The network is divided into global feature extraction module, multi‐scale feature extraction module and deep fusion module. The global feature extraction module extracts global features that characterize the contour; multi‐scale feature extraction module extracts features at different scales to improve learning accuracy; in the deep fusion module, the convolutional layer extracts the local features that describe the image content, and then the local features and the global features are merged through skip connections. Comparative experiments were carried out on artificially synthesized fog images and real fog images. The experimental results show that the algorithm proposed here can achieve the ideal dehazing effect, and is superior to other comparison algorithms in subjective and objective aspects.