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

Prof. Afshan Jabeen; Shifa Siddiqui; Abdul Rehman Khan; Abdul Mustaqid Sheikh; Sayyad Sayma Sadaf; Moin Sheikh

doi:10.32628/IJSRST25121176

Authors

Prof. Afshan Jabeen Department of Artificial Intelligence and Data Science, Anjuman College of Engineering and Technology, Nagpur, Maharashtra, India Author
Shifa Siddiqui Department of Artificial Intelligence and Data Science, Anjuman College of Engineering and Technology, Nagpur, Maharashtra, India Author
Abdul Rehman Khan Department of Artificial Intelligence and Data Science, Anjuman College of Engineering and Technology, Nagpur, Maharashtra, India Author
Abdul Mustaqid Sheikh Department of Artificial Intelligence and Data Science, Anjuman College of Engineering and Technology, Nagpur, Maharashtra, India Author
Sayyad Sayma Sadaf Department of Artificial Intelligence and Data Science, Anjuman College of Engineering and Technology, Nagpur, Maharashtra, India Author
Moin Sheikh Department of Artificial Intelligence and Data Science, Anjuman College of Engineering and Technology, Nagpur, Maharashtra, India Author

DOI:

https://doi.org/10.32628/IJSRST25121176

Keywords:

Surveillance footage, Environmental factors, Noise reduction, Fog removal, Video enhancement

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.

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