Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing

Code

• github.com/Seanforfun/GMAN_Net_Haze_Removal
  OfficialIn papertf★ 0
• github.com/Seanforfun/Deep-Learning
  Officialtf★ 0
• github.com/sanchitvj/Image-Dehazing-using-GMAN-net
  tf★ 0

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

Tasks

Benchmark Results

Reproductions