STEREOFOG -- Computational DeFogging via Image-to-Image Translation on a
  real-world Dataset
Anton Pollak Rajesh Menon
Image-to-Image translation (I2I) is a subtype of Machine Learning (ML) that
has tremendous potential in applications where two domains of images and the
need for translation between the two exist, such as the removal of fog. For
example, this could be useful for autonomous vehicles, which currently struggle
with adverse weather conditions like fog. However, datasets for I2I tasks are
not abundant and typically hard to acquire. Here, we introduce STEREOFOG, a
dataset comprised of $10,067$ paired fogged and clear images, captured using a
custom-built device, with the purpose of exploring I2I's potential in this
domain. It is the only real-world dataset of this kind to the best of our
knowledge. Furthermore, we apply and optimize the pix2pix I2I ML framework to
this dataset. With the final model achieving an average Complex
Wavelet-Structural Similarity (CW-SSIM) score of $0.76$, we prove the
technique's suitability for the problem.