Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing

October 05, 2018 · Entered Twilight · 🏛 arXiv.org

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Repo contents: GMAN_distribute_training_version, GMEAN_NET, README.md, Results

Authors Zheng Liu, Botao Xiao, Muhammad Alrabeiah, Keyan Wang, Jun Chen arXiv ID 1810.02862 Category cs.CV: Computer Vision Citations 20 Venue arXiv.org Repository https://github.com/Seanforfun/GMAN_Net_Haze_Removal ⭐ 42 Last Checked 1 month ago

Abstract

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