A New Real-World Video Dataset for the Comparison of Defogging Algorithms
Alexandra Duminil, Jean-Philippe Tarel, Roland Br\'emond

TL;DR
This paper introduces VIREDA, a new real-world video dataset with varying fog densities and clear ground truths, to benchmark and develop video defogging algorithms, emphasizing the potential of Transformer-based models.
Contribution
The paper presents VIREDA, a novel dataset for real-world video defogging, and explores Transformer architectures for improved defogging performance.
Findings
VIREDA provides diverse foggy videos with ground truths for benchmarking.
Transformer-based models show promising results on the VIREDA dataset.
The dataset facilitates development of more effective video defogging algorithms.
Abstract
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…
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Taxonomy
TopicsImage Enhancement Techniques · Advanced Image Processing Techniques · Advanced Vision and Imaging
MethodsBalanced Selection
