Underwater image enhancement of ROV using modified WaterNet

Si Wu; Yuepeng Chen; Xu Yang

doi:10.1117/12.3034944

11 July 2024 Underwater image enhancement of ROV using modified WaterNet

Si Wu, Yuepeng Chen, Xu Yang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132103C (2024) https://doi.org/10.1117/12.3034944
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

With the development of marine engineering and remote operated vehicles, underwater image enhancement is receiving more and more attention. In recent years, there have been many methods for underwater image enhancement. In particular, various convolutional neural networks (CNN) have been applied in the field of underwater image enhancement. However, the underwater imaging environment is complex and changeable. In the underwater environment, red light attenuates faster than blue-green light, and suspended impurities and various algae in the water will also affect the propagation of light. Therefore, the collected underwater light images often suffer from varying degrees of degradation. This makes it more challenging for CNN models to enhance images in different environments. This paper modifies the architecture of Water-Net, and improves the output of the confidence map. Experimental results on the Underwater Image Enhancement Benchmark (UIEB) show that our network achieves better results than other algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Si Wu, Yuepeng Chen, and Xu Yang "Underwater image enhancement of ROV using modified WaterNet", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132103C (11 July 2024); https://doi.org/10.1117/12.3034944

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KEYWORDS

Image enhancement

Image processing

Education and training

Image quality

Convolutional neural networks

Image fusion

Neural networks

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