Papers › Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing

Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing

5 Oct 2018arXiv:1810.02862archive 2025-07-28

Zheng Liu, Botao Xiao, Muhammad Alrabeiah, Keyan Wang, Jun Chen

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

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