A Hybrid Weiner Filter with MR-CNN for Object Detection in Underwater Image Processing

A Hybrid Weiner Filter with MR-CNN for Object Detection in Underwater Image Processing

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© 2024 by IJETT Journal
Volume-72 Issue-1
Year of Publication : 2024
Author : Hemalatha, Saravanan
DOI : 10.14445/22315381/IJETT-V72I1P101

How to Cite?

Hemalatha, Saravanan, "A Hybrid Weiner Filter with MR-CNN for Object Detection in Underwater Image Processing," International Journal of Engineering Trends and Technology, vol. 72, no. 1, pp. 1-10, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I1P101

Abstract
Underwater image processing poses unique challenges due to the presence of various degradation factors such as color attenuation, backscatter, and noise. Object detection in underwater images is particularly challenging as these factors can affect the visibility and clarity of objects. In this study, we present a unique approach that combines the Weiner filter with a Multi-Resolution Convolutional Neural Network (MR-CNN) for a method for detecting objects underwater image processing to enhance object-detecting ability, and we integrate the Weiner filter with an MR-CNN. The MR-CNN utilizes multiple resolutions to capture and analyze different levels of information in the image. This multi-resolution approach allows for better extraction of features at various scales, enabling the network to detect accurately. The combination of the Weiner filter and the MR-CNN significantly improves the object detection accuracy in underwater images compared to traditional methods. The results highlight the potential for practical applications in underwater research, exploration, and surveillance domains

Keywords
Underwater image processing, Weiner filter, Multi-Resolution Convolutional Neural Network, Object detection, Surveillance domains.

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