Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing

5 Oct 2018  ·  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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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Dehazing SOTS Indoor GMAN PSNR 20.53 # 12
SSIM 0.8081 # 13
Image Dehazing SOTS Outdoor GMAN PSNR 28.19 # 6
SSIM 0.9638 # 5

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