Atmosclear (Ai-Ml De-Haze/De-Smoking Algorithm)

Afshan Jabeen, Shifa Siddiqui, Abdul Rehman Khan, Abdul Mustaqid Sheikh, Sayyad Sayma Sadaf and Moin Sheikh

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 1, 261-267

Abstract: Surveillance footage quality is often degraded by environmental factors like noise and fog, impacting monitoring accuracy. AtmosClear is an AI-ML solution designed to enhance video clarity in challenging conditions. It employs deep learning techniques, including Convolutional Neural Networks (CNNs) for noise reduction and Generative Adversarial Networks (GANs) for fog removal, optimizing in real time to meet the high-speed demands of surveillance systems. AtmosClear's improvements in video clarity benefit both human operators and automated detection systems, enabling more accurate threat assessment across law enforcement, transportation security, and other surveillance applications.

Keywords: Surveillance footage; Environmental factors; Noise reduction; Fog removal; Video enhancement (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i1:id:563

DOI: 10.32628/IJSRST25121176

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