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Underwater Diver Image Enhancement via Dual-Guided Filtering

Jingchun Zhou1,*, Taian Shi1, Weishi Zhang1,*, Weishen Chu2

1 College of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China
2 Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA

* Corresponding Authors: Jingchun Zhou. Email: email; Weishi Zhang. Email: email

(This article belongs to this Special Issue: Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)

Computer Modeling in Engineering & Sciences 2022, 131(2), 1063-1081.


The scattering and absorption of light propagating underwater cause the underwater images to present low contrast, color deviation, and loss of details, which in turn make human posture recognition challenging. To address these issues, this study introduced the dual-guided filtering technique and developed an underwater diver image improvement method. First, the color distortion of the underwater diver image was solved using white balance technology to obtain a color-corrected image. Second, dual-guided filtering was applied to the white balanced image to correct the distorted color and enhance its details. Four feature weight maps of the two images were then calculated, and two normalized weight maps were constructed for multi-scale fusion using normalization. To better preserve the obtained image details, the fusion image was histogram-stretched to obtain the final enhanced result. The experimental results validated that this method has improved the accuracy of underwater human posture recognition.


Cite This Article

Zhou, J., Shi, T., Zhang, W., Chu, W. (2022). Underwater Diver Image Enhancement via Dual-Guided Filtering. CMES-Computer Modeling in Engineering & Sciences, 131(2), 1063–1081.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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