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

  • Shengdong Zhang
  • Fazhi He
  • Wenqi Ren
  • Jian Yao
Original Article

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.

Keywords

Joint learning Dehazing Image detail estimating Non-local regularization Transmission estimating 

Notes

Acknowledgements

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

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Shengdong Zhang
    • 1
  • Fazhi He
    • 1
  • Wenqi Ren
    • 2
  • Jian Yao
    • 3
  1. 1.School of Computer ScienceWuhan UniversityWuhanChina
  2. 2.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  3. 3.School of Remote Sensing and Information EngineeringWuhan UniversityWuhanChina

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