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Gobinda Bauri, Arun Kumar Jhapate, Ritu Shrivastava
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- Research Scholar, Assistant Professor, Associate Professor, Department of Computer Science and Enginnering, Sagar Institute of Research & Technology, Bhopal, Department of Computer Science and Enginnering, Sagar Institute of Research & Technology, Bhopal, Department of Computer Science and Enginnering, Sagar Institute of Research & Technology, Bhopal, Madhya Pradesh, Madhya Pradesh, Madhya Pradesh, India, India, India
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Abstract
nThis review explores the application of artificial intelligence (AI) techniques for image dehazing, addressing the pervasive challenge of enhancing image quality in hazy or foggy conditions. Traditional dehazing methods and their role as a foundation for AI-based approaches are discussed. Deep learning-based methods, including single-image and multi-image dehazing, are examined, highlighting their strengths and limitations. Data-driven approaches, leveraging large-scale datasets and domain adaptation, are also investigated. Furthermore, the review outlines the challenges in real-time processing, robustness, explainability, and real-world deployment of AI-based dehazing solutions. As AI technology advances, it is expected that image dehazing will find practical applications across various domains, making it essential to overcome the existing challenges to fully unlock its potential.
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Keywords: Artificial Intelligence, Dehazing, Image, Blur, Deep learning, Multi-Scale
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Mechanical Dynamics and Systems Analysis(ijmdsa)]
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References
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International Journal of Mechanical Dynamics and Systems Analysis
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| Volume | 01 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | March 15, 2024 | |
| Accepted | March 28, 2024 | |
| Published | April 30, 2024 |
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