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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Multispectral Image dehazing using Convolution Neural Networks |
| Authors Name: | Aditya Anand , Hari Lunavath , Dr. Vijay Bhardwaj |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_210074
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| Published Paper Id: | IJNRD2312009 |
| Published In: | Volume 8 Issue 12, December-2023 |
| DOI: | |
| Abstract: | Multispectral Image Dehazing (MID) stands as a pivotal research domain within computer vision and remote sensing, tackling the persistent challenge of atmospheric haze that significantly degrades image quality across diverse applications. The presence of haze, stemming from airborne particles and environmental factors, leads to reduced visibility, diminished contrasts, and an overall decline in image fidelity. This article provides a comprehensive exploration of advanced techniques dedicated to alleviating the detrimental impacts of haze in multispectral imagery. The proposed methodologies capitalize on the distinctive attributes of multispectral data, extracting information from various spectral bands to amplify the precision and effectiveness of dehazing algorithms. Traditional single-band dehazing methods often prove inadequate in intricate scenarios where multiple spectral channels offer crucial contextual insights for enhanced scene comprehension. Through the integration of multispectral information, these approaches exhibit superior capabilities in restoring clarity and contrast to hazy images. This makes them well-suited for applications spanning satellite imaging, environmental monitoring, and autonomous navigation. The article delves into a review and analysis of cutting-edge multispectral dehazing algorithms, shedding light on their merits and constraints. Furthermore, it explores challenges posed by real-world situations, encompassing diverse atmospheric conditions and scene characteristics. The discussion extends to evaluation metrics and benchmark datasets, facilitating standardized comparisons of performance. The insights presented in this exploration contribute to the ongoing endeavors aimed at advancing the realm of multispectral image dehazing, fostering innovation and pragmatic solutions to enhance image quality under challenging environmental circumstances. |
| Keywords: | Multispectral Image Dehazing, Atmospheric Haze, Computer Vision, Remote Sensing, Image Quality Enhancement, Spectral Bands |
| Cite Article: | "Multispectral Image dehazing using Convolution Neural Networks", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 12, page no.a48-a53, December-2023, Available :http://www.ijnrd.org/papers/IJNRD2312009.pdf |
| Downloads: | 000118764 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2312009 Registration ID: 210074 Published In: Volume 8 Issue 12, December-2023 DOI (Digital Object Identifier): Page No: a48-a53 Country: Chandigarh, Punjab, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2312009 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2312009 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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