Conditional Wasserstein Generative Adversarial Networks for Image Dehazing
Aug 1, 2018
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1 min read
On the left is an image affected by fog, on the right is an image cleared by our algorithm.Project Supervisor: Professor Sudipta Mukhopadhyay
Achieved state-of-the-art results by training a conditional Wasserstein GAN using the pix2pix model for single image dehazing, with perceptual loss, MSE loss, L1 loss, and texture loss, on the D-Hazy and O-Haze fog datasets, using Pytorch as the programming library.
