PoS - Proceedings of Science
Volume 429 - The 6th International Workshop on Deep Learning in Computational Physics (DLCP2022) - Track3. Machine Learning in Natural Sciences
Hazy images dataset with localized light sources for experimental evaluation of dehazing methods
A.I. Filin*, A.V. Kopylov, O. Seredin and I. Gracheva
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Pre-published on: November 22, 2022
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Image haze removal methods have taken increasing attention of researchers. At the same time, an objective comparison of haze removal methods struggles because of the lack of real data. Capturing pairs of images of the same scene with presence/absence of haze in real environment is a very complicated task. Therefore, the most of modern image haze removal datasets contain artificial images, generated by some model of atmospheric scattering and known scene depth. Among the few real datasets, there are almost no datasets consisting of images obtained in low light conditions with artificial light sources, which allows evaluating the effectiveness of nighttime haze removal methods. In this paper, we present such dataset, consisting of images of 2 scenes at 4 lighting levels and 4 levels of haze density. The scenes has varying "complexity" – the first scene consists of objects with a simpler texture and shape (smooth, rectangular and round objects); the second scene is more complex – it consists of objects with small details, protruding parts and localized light sources. All images were taken indoors in a controlled environment. An experimental evaluation of state-of-the-art haze removal methods was carried out on the collected dataset.
DOI: https://doi.org/10.22323/1.429.0019
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