Conditional Wasserstein Generative Adversarial Networks for Image Dehazing
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.
Joshua Peter Ebenezer
Senior Research Engineer at Samsung’s Mobile Processor Innovation Lab
My research interests include image and video quality assessment, natural video statistics, and deep learning.