Dehazing algorithm based on multi-scale feature extraction

Lingjun Chen; Caidan Zhao; Xiangyu Huang; YiLin Wang; Junjie Deng

doi:10.1117/12.2636944

29 April 2022 Dehazing algorithm based on multi-scale feature extraction

Lingjun Chen, Caidan Zhao, Xiangyu Huang, YiLin Wang, Junjie Deng

Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 122471R (2022) https://doi.org/10.1117/12.2636944
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China

Abstract

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.

Citation Download Citation

Lingjun Chen, Caidan Zhao, Xiangyu Huang, YiLin Wang, and Junjie Deng "Dehazing algorithm based on multi-scale feature extraction", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 122471R (29 April 2022); https://doi.org/10.1117/12.2636944

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