Dehazing algorithm based on multi-scale feature extraction
Lingjun Chen, Caidan Zhao, Xiangyu Huang, Yilin Wang, Junjie Deng · 2022
Fog seriously affects the visual perception of human eyes and reduces the quality of captured images. This paper proposes a dehazing Generative Adversarial Network based on multi-scale feature extraction. The method is an end-toend dehazing network that avoids the dependence on physical models. By adding the edge feature extraction module to the generator network to obtain the high-frequency information of the foggy image, the attention to the edge information of the image is effectively improved. In addition, the multi-scale features of the image are extracted, and then the foggy image is enhanced by a unique feature fusion mechanism. The discriminator network uses the global discriminator and the local discriminator to make a joint judgement, which further improves the dehazing performance. Compared with state-of-the-art approaches available in the literature, the algorithm proposed in this paper obtains better subjective and objective image quality evaluation on the cityscape foggy image synthesis dataset.