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GAN-based underwater image enhancement and scene classification using transfer learning.

Amani Homoud^1,2, Saptarshi Das^2,3

¹Mathematics Department, College of Science, Jazan University, Jazan, Saudi Arabia.

|March 27, 2026

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This study enhances underwater image quality using Gray World, Histogram Equalization, and Canny Edge Detection. Deep learning models like VGG16, ResNet50, and DenseNet121 accurately classify marine species for conservation.

Area of Science:

Marine Biology
Computer Vision
Image Processing

Background:

Underwater image quality is often degraded by low light, color distortion, and limited visibility.
These challenges hinder accurate marine species identification and object detection.
Existing methods require enhancement for effective underwater analysis.

Purpose of the Study:

To develop and evaluate a pipeline for enhancing underwater video quality.
To improve the accuracy of marine species classification using deep learning.
To support marine ecosystem understanding and conservation efforts.

Main Methods:

Image enhancement using Gray World (GW), Histogram Equalization (HE), and Contrast Limited Adaptive Histogram Equalization (CLAHE).
Feature extraction with Canny Edge Detection.

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Marine species classification via transfer learning with VGG16, ResNet50, and DenseNet121 models.

Utilized Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) for noise reduction.

Main Results:

The proposed pipeline effectively enhances underwater image quality, addressing color distortion and low contrast.
Deep learning models achieved accurate classification of fish, coral reefs, and sea turtles.
Canny Edge Detection successfully identified key features in enhanced images.

Conclusions:

Enhanced underwater image quality significantly improves marine species classification accuracy.
The developed techniques offer valuable tools for marine biology, environmental monitoring, and autonomous systems.
This research advances underwater video analysis for ecological research and conservation.