OCEAN EXPLORATION (Depth Analysis,Image Enhancement and Classification)

OCEAN EXPLORATION (Depth Analysis,Image Enhancement and Classification)

Registration ID: IJNRD_211844

Published ID: IJNRD2312410

DOI: Click Here to Get

Vummidi Umesh Krishna Rao , Gadi Jagadeesh

Weak Illumination, Image processing, Transmission map, dark channel prior, prominence

The uncharted depths of the ocean harbor a wealth of valuable resources and undiscovered species. However, the challenges associated with exploring these environments are extensive and intricate. In recent years, the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has exhibited significant potential in revolutionizing ocean exploration. This research presents an overview of the current state of utilizing AI and ML in ocean exploration, elucidating key advancements, challenges, and potential future directions, while also scrutinizing the depths of oceans and classifying different species. Within an underwater environment, the need for weak illumination and low-quality image enhancement as a pre-processing procedure is imperative for effective underwater vision. This study addresses the prominence of Underwater Image Enhancement (UIEB) in marine engineering and aquatic robotics. Various algorithms, such as XGBoost, Random Forest, CNN, and MFPF, have been proposed for underwater image enhancement. Notably, among these algorithms, MFPF consistently demonstrates superior results. Nevertheless, these algorithms primarily undergo evaluation using synthetic datasets or a limited selection of real-world images, leaving uncertainties regarding their performance on images obtained in the wild and the ability to assess advancements in the field. To bridge this knowledge gap, this research introduces a comprehensive perceptual study and analysis of underwater image enhancement utilizing large-scale real-world images. The constructed UIEB incorporates real-world underwater images with corresponding reference images, employing a red channel prior model for underwater environments based on dark channel prior

Published Paper   E-Certificate

"OCEAN EXPLORATION (Depth Analysis,Image Enhancement and Classification)", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 12, page no.e92-e109, December-2023, Available :https://ijnrd.org/papers/IJNRD2312410.pdf

Volume 8 Issue 12, December-2023

Pages : e92-e109

Paper Reg. ID: IJNRD_211844

Published Paper Id: IJNRD2312410

Downloads: 000121163

Research Area: Engineering

Country: visakhapatnam, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2312410.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2312410

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

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

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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