Low levels of lighting in images and videos may lead to poor results in segmentation, detection, tracking, among numerous other computer vision tasks. Deep-sea camera systems, such as those deployed on the Ocean Networks Canada (ONC) cabled ocean observatories, use artificial lighting to illuminate and capture videos of deep-water biological environments. When these lighting systems fail, the resulting images become hard to interpret or even completely useless because of their lighting levels. This paper proposes an effective framework to enhance the lighting levels of underwater images, increasing the number of visible, meaningful features. The process involves the dehazing of images using a dark channel prior and fast guided filters.
CITATION STYLE
Marques, T. P., Albu, A. B., & Hoeberechts, M. (2019). Enhancement of Low-Lighting Underwater Images Using Dark Channel Prior and Fast Guided Filters. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11188 LNCS, pp. 55–65). Springer Verlag. https://doi.org/10.1007/978-3-030-05792-3_6
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