Under Water Image Enhancement using Custom VGG19

Authors

  • Amulya K., Venkata Raju K.

Keywords:

Custom VGG-19 Architecture, Deep Learning, Encoder-Decoder Network, High-Resolution Imaging, Relational Bilevel Aggregation Graph Convolutional Network, Underwater Image Enhancement, Variational Bayesian-based Robust Adaptive Filtering.

Abstract

This research introduces an innovative approach to underwater image processing through the use of a modified VGG-19 architecture, renowned for its exceptional image processing capabilities using Variational Bayesian-based Robust Adaptive Filtering (VBRAF). At the heart of this approach is the integration of custom layers within the Relational Bilevel Aggregation Graph Convolutional Network (RBAGCN)-VGG-19 frameworks. Enhancing the model's adaptability to the unique challenges of underwater condition using image processing; this method incorporates a specialized encoder based on the VGG-19 architecture, enriched with additional custom layers for detailed from underwater imagery. The decoder component then reconstructs these details into images of higher clarity and resolution. A notable aspect of method is the inclusion of a max unpooling mechanism, which streamlines the processing of complex deep learning models and improves the clarity of lower-resolution. This research marks a significant advancement in underwater image processing, setting a new standard for high-quality underwater photography and marine research, facilitated by the customized VGG-19 architecture.

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References

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Published

16.03.2024

How to Cite

Venkata Raju K., A. K. . (2024). Under Water Image Enhancement using Custom VGG19. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 1093–1100. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5388

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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