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
@article{arxiv.1810.02862,
title = {Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing},
author = {Zheng Liu and Botao Xiao and Muhammad Alrabeiah and Keyan Wang and Jun Chen},
journal= {arXiv preprint arXiv:1810.02862},
year = {2019}
}