What question did this study set out to answer?

The central aim is to enhance underwater images and improve classification accuracy of marine species using advanced techniques.

March 29, 2026Open Access

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

Key Points

The central aim is to enhance underwater images and improve classification accuracy of marine species using advanced techniques.
Implemented Gray World algorithm for initial image enhancement
Compared Histogram Equalization and CLAHE for superior image quality
Utilized Canny Edge Detection for feature identification
Employed transfer learning with VGG16, ResNet50, and DenseNet121 for classification of underwater species
Enhanced image quality significantly improved object detection accuracy
CLAHE outperformed traditional Histogram Equalization in specific cases
Transfer learning models achieved high classifier accuracy, aiding marine species identification

Abstract

This paper provides an exploratory analysis of underwater video analysis techniques to enhance image quality and facilitate accurate classification of different marine species. Our methodology progresses through several steps, beginning with the quality of underwater images that might be reduced by variables such as decreased light intensity, color modification, and limited visibility. These attributes pose significant challenges to develop accurate object detection methods. This paper outlines the processing pipeline employed to enhance the quality of images from underwater videos and facilitate precise object detection. First, we use the Gray World (GW) algorithm for image enhancement, effectively mitigating the challenges of aquatic environment, such as color distortion and low contrast. Subsequently, we compare the traditional Histogram Equalization (HE) and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms to assess their efficacy in enhancing underwater image quality. Next, Canny Edge Detection is utilized to identify the prominent features in the enhanced images, aiding in subsequent classification tasks. Next, three state-of-the-art deep learning models, Visual Geometry Group 16-layer network (VGG16), 50-layer Residual Network (ResNet50), and 121-layer Densely Connected Convolutional Network (DenseNet121), are leveraged through transfer learning to classify underwater species, including fish, coral reefs, and sea turtles. Finally, by enhancing the visual quality of underwater images, our research contributes to better understanding of the underwater ecosystem and supports conservation efforts. Enhanced Super-Resolution GAN (ESRGAN) is a superior Generative Adversarial Network (GAN) technique to improve the quality of noisy images. This paper contributes to advancing the field of underwater image and video analysis, offering valuable insights for applications in marine biology, environmental monitoring, underwater robotics, and autonomous navigation.

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

Key Points

Abstract

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