UWM-Net: A Mixture Density Network Approach with Minimal Dataset Requirements for Underwater Image Enhancement

Jun Huang1; Zongze Li1; Ruihao Zheng1; Zhenkun Wang2

10.1109/CAI59869.2024.00269

2024-06-27

979-8-3503-5410-2

2024 IEEE Conference on Artificial Intelligence (CAI)

25-27 June 2024

Singapore, Singapore

The learning-based underwater image enhancement, which is suitable for batch processing, is a pivotal research direction in underwater image processing. Extensive paired image data are required in existing learning-based methods, which necessitate considerable preprocessing and hinder the application of these methods. To address these limitations, we propose a semi-supervised approach called UWM-Net: firstly, we use a compact dataset of underwater image pairs to train the Mixture Density Network (MDN) with an underwater scene setting; subsequently, U-Net can learn underwater image enhancement more efficiently. The MDN can transform standard images into underwater scenes, reducing the reliance on paired data and making much smaller training datasets. In experimental studies, UWM-Net using only 18 pairs of underwater image data achieves highly competitive results in terms of 3 metrics compared with advanced models.

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EI

1.School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China
2.Department of Computer Science and Engineering, School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China

Jun Huang,Zongze Li,Ruihao Zheng,et al. UWM-Net: A Mixture Density Network Approach with Minimal Dataset Requirements for Underwater Image Enhancement[C],2024.