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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: | OCEAN EXPLORATION (Depth Analysis,Image Enhancement and Classification) |
| Authors Name: | Vummidi Umesh Krishna Rao , Gadi Jagadeesh |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_211844
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| Published Paper Id: | IJNRD2312410 |
| Published In: | Volume 8 Issue 12, December-2023 |
| DOI: | |
| Abstract: | 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 |
| Keywords: | Weak Illumination, Image processing, Transmission map, dark channel prior, prominence |
| Cite Article: | "OCEAN EXPLORATION (Depth Analysis,Image Enhancement and Classification)", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 12, page no.e92-e109, December-2023, Available :http://www.ijnrd.org/papers/IJNRD2312410.pdf |
| Downloads: | 000118757 |
| 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:IJNRD2312410 Registration ID: 211844 Published In: Volume 8 Issue 12, December-2023 DOI (Digital Object Identifier): Page No: e92-e109 Country: visakhapatnam, Andhra Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2312410 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2312410 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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