A New Real-World Video Dataset for the Comparison of Defogging
  Algorithms
Alexandra Duminil Jean-Philippe Tarel Roland Brémond
Video restoration for noise removal, deblurring or super-resolution is
attracting more and more attention in the fields of image processing and
computer vision. Works on video restoration with data-driven approaches for fog
removal are rare however, due to the lack of datasets containing videos in both
clear and foggy conditions which are required for deep learning and
benchmarking. A new dataset, called REVIDE, was recently proposed for just that
purpose. In this paper, we implement the same approach by proposing a new
REal-world VIdeo dataset for the comparison of Defogging Algorithms (VIREDA),
with various fog densities and ground truths without fog. This small database
can serve as a test base for defogging algorithms. A video defogging algorithm
is also mentioned (still under development), with the key idea of using
temporal redundancy to minimize artefacts and exposure variations between
frames. Inspired by the success of Transformers architecture in deep learning
for various applications, we select this kind of architecture in a neural
network to show the relevance of the proposed dataset.