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An end-to-end single image dehazing network based on U-net

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Abstract

Single image haze removal is always significant for computer advanced vision tasks, while it is also a challenging problem. In this paper, inspired of the recent methods, we proposed an end-to-end network with encoding–decoding structure and jumping layers for single image dehazing. The network combined the advantages of VGG16 and the U-net and adopted different jumping layers to retain most of the image feature information. In order to clarify the image features like contrast and color distribution, the scale-invariant loss function and the proposed histogram loss function were used. We compared the algorithm with the several state-of-the-art algorithms qualitatively and quantitatively. Experimental results demonstrated that the proposed algorithm has achieved favorable dehazing results on both indoor and outdoor synthetic hazy testing set and real-world set. In particular, it obtained the better dehazing results for the slight hazy conditions than other density of haze.

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Acknowledgements

This research was funded by the National Natural Science Foundation of China (Grant No. 32071680).

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Authors and Affiliations

  1. School of Technology, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing, 100083, China

    Yu Miao, Xixuan Zhao & Jiangming Kan

  2. Key Laboratory of State Forestry Administration on Forestry Equipment and Automation, No. 35 Qinghua East Road, Haidian District, Beijing, 100083, China

    Yu Miao, Xixuan Zhao & Jiangming Kan

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  1. Yu Miao
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  2. Xixuan Zhao
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Correspondence to Xixuan Zhao.

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Miao, Y., Zhao, X. & Kan, J. An end-to-end single image dehazing network based on U-net. SIViP 16, 1739–1746 (2022). https://doi.org/10.1007/s11760-021-02129-4

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