  Haze and smog are among the most common environmental factors impacting image
quality and, therefore, image analysis. This paper proposes an end-to-end
generative method for image dehazing. It is based on designing a fully
convolutional neural network to recognize haze structures in input images and
restore clear, haze-free images. The proposed method is agnostic in the sense
that it does not explore the atmosphere scattering model. Somewhat
surprisingly, it achieves superior performance relative to all existing
state-of-the-art methods for image dehazing even on SOTS outdoor images, which
are synthesized using the atmosphere scattering model.
  Project detail and code can be found here:
https://github.com/Seanforfun/GMAN_Net_Haze_Removal
