Single Image Dehazing via Image Generating

  • Shengdong Zhang
  • Jian Yao
  • Edel B. Garcia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)


Outdoor images taken in bad weather conditions often suffer from poor visibility. However, single image haze removal is an ill-posed problem, because the number of the equations is smaller than the number of unknowns. In this paper, a deep learning-based method, called Dehaze CNN, is proposed to estimate a clear image patch from a hazy image patch, which can be used to reconstruct a haze-free image. Our method recovers a clear image by a learning model containing no hazy information. Our method also adopts Deep Convolution Neural Networks which takes the patch atom that can be used to generate hazy image patches and haze-free ones as the input and outputs the corresponding haze-free patch. Then we reconstruct a haze-free image from those patches. Finally, we remove the color distortion in the haze-free image via contextual regularization effectively. Experimental results show that the proposed method outperforms the state-of-the-art haze removal methods.


Haze removal Image restoration Deep Convolution Neural Networks 



This work was partially supported by the National Natural Science Foundation of China (Project No. 41571436), the Hubei Province Science and Technology Support Program, China (Project No. 2015BAA027), the National Natural Science Foundation of China under Grant 91438203, LIESMARS Special Research Funding, and the South Wisdom Valley Innovative Research Team Program.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Remote Sensing and Information EngineeringWuhan UniversityWuhanChina
  2. 2.Advanced Technology Applications CenterHavanaCuba

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