Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4367
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dc.contributor.authorPradhan, Soumen-
dc.contributor.authorAman, Mritunjay-
dc.contributor.authorReddy, Adamala Sai Balaji-
dc.contributor.authorMukherjee, Shyamapada-
dc.date.accessioned2024-02-02T12:54:40Z-
dc.date.available2024-02-02T12:54:40Z-
dc.date.issued2023-11-
dc.identifier.citation4th International Conference on Computation, Automation and Knowledge Management(ICCAKM), Dubai, UAE, 14-16 November 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4367-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractWeather conditions such as Haze, Fog reduce visi-bility making it difficult to observe the surrounding environment. This makes drivers prone to accidents in such situations. Most of the existing methods for dehazing use models that work upon single-image dehazing. Dehazing in real-time proves a much bigger challenge as it demands efficient handling of the constant stream of frames, as well as a lightweight dehazing algorithm. Further, there is the need for some decision-making parameter that determines whether dehazing is needed. This paper provides a novel framework that works at dehazing in real-time using Dark Channel Prior(DCP), and is able to work on minimal hardware such as smartphones.en_US
dc.subjectdehazingen_US
dc.subjectdark channel prioren_US
dc.subjectairlighten_US
dc.subjecttransmis-sion mapen_US
dc.subjectfog densityen_US
dc.subjectframe rateen_US
dc.titleDriver Assistance System based on Real-time Dehazingen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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