Multispectral Image dehazing using Convolution Neural Networks

Registration ID: IJNRD_210074

Published ID: IJNRD2312009

DOI: Click Here to Get

Aditya Anand , Hari Lunavath , Dr. Vijay Bhardwaj

Multispectral Image Dehazing, Atmospheric Haze, Computer Vision, Remote Sensing, Image Quality Enhancement, Spectral Bands

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.

Published Paper   E-Certificate

Aditya Anand, Hari Lunavath, & Dr. Vijay Bhardwaj (December-2023). Multispectral Image dehazing using Convolution Neural Networks. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(12), a48-a53. https://ijnrd.org/papers/IJNRD2312009.pdf

Volume 8 Issue 12, December-2023

Pages : a48-a53

Paper Reg. ID: IJNRD_210074

Published Paper Id: IJNRD2312009

Downloads: 000121983

Research Area: Engineering

Country: Chandigarh, Punjab, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2312009.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2312009

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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Call For Paper - Volume 10 | Issue 10 | October 2025

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