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Joint learning of image detail and transmission map for single image dehazing

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Abstract

Single image haze removal is an important task in computer vision. However, haze removal is an extremely challenging problem due to its massively ill-posed, which is that at each pixel we must estimate the transmission and the global atmospheric light from a single color measurement. In this paper, we propose a new deep learning-based method for removing haze from single input image. First, we estimate a transmission map via joint estimation of clear image detail and transmission map, which is different from traditional methods only estimating a transmission map for a hazy image. Second, we use a global regularization method to eliminate the halos and artifacts. Experimental results on synthetic dataset and real-world images show our method outperforms the other state-of-the-art methods.

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Acknowledgements

This study was funded by National Natural Science Foundation of China (Grant Numbers 61472289 and 41571436).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Shengdong Zhang & Fazhi He

  2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Wenqi Ren

  3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

    Jian Yao

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  1. Shengdong Zhang
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  2. Fazhi He
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  3. Wenqi Ren
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Correspondence to Fazhi He.

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Zhang, S., He, F., Ren, W. et al. Joint learning of image detail and transmission map for single image dehazing. Vis Comput 36, 305–316 (2020). https://doi.org/10.1007/s00371-018-1612-9

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  • DOI: https://doi.org/10.1007/s00371-018-1612-9

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