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Research Article |

An Adaptive Technique for Underwater Image Enhancement with CNN and Ensemble Classifier

Author(s): Yogesh K. Gupta1, and Khushboo Saxena2

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 4

Publisher : FOREX Publication

Published : 10 November 2022

e-ISSN : 2347-470X

Page(s) : 932-938

DOI: https://doi.org/10.37391/IJEER.100430

Abstract

Image Restoration is a significant phase to process images for their enhancement. Underwater photographs are subject to quality issues such as blurry photos, poor contrast, uneven lighting, etc. Image processing is crucial in the processing of these degraded images. This research introduced an ensemble-based classifier based on the bagging approach to enhance UW images. The support vector machine and random forest classifiers serve as the ensemble classifier's main classifiers. Additionally, to complement the feature optimization technique, the proposed ensemble classifier leverages particle swarm optimization. The feature selection method for the classifier is improved by the feature optimization process. To validate results, underwater images are collected by the Kaggle repository. In this process, Extreme Learning (EL) and Convolution Neural Network (CNN) are compared with suggested algorithms. The simulation results indicate that the proposed algorithm outperformed the existing work.

Keywords: Image Enhancement, CNN, Ensemble Classifier.




Yogesh K. Gupta, Department of Computer Science, Banasthali Vidyapith Niwai, Rajasthan, India; Email: hemub09@gmail.com

Khushboo Saxena*, Department of Computer Science, Banasthali Vidyapith Niwai, Rajasthan, India; Email: dpkadam@gmail.com

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Yogesh K. Gupta, Khushboo Saxena (2022), An Adaptive Technique for Underwater Image Enhancement with CNN and Ensemble Classifier. IJEER 10(4), 932-938. DOI: 10.37391/IJEER.100430.