8 January 2024 Multiscale luminance adjustment-guided fusion for the dehazing of underwater images
Huipu Xu, Min Wang, Shuo Chen
Author Affiliations +
Abstract

Due to absorption and scattering effects in the underwater medium, acquired underwater images often suffer from hazy content and color casts, which lead to significant degradation of visual quality. In this work, a dehazing model is proposed; it is divided into three parts and effectively eliminates the problem of visual degradation caused by hazy content. The attenuation characteristics of light at different wavelengths are different, which usually leads to significant color bias in the obtained underwater images. Therefore, underwater images are first color corrected, namely by red channel compensation and white balance processing. Next, because the original hazy images are usually underexposed, we manually adjust the luminance of the images. The resulting multi-level luminance images are then fused using the Gaussian–Laplacian pyramid scheme to produce clear underwater images. To verify the validity of our method, we evaluate several representative underwater image datasets and compare our method with several advanced traditional methods and deep learning methods developed in recent years. Our method shows excellent results in both qualitative analysis and quantitative comparison.

© 2024 SPIE and IS&T
Huipu Xu, Min Wang, and Shuo Chen "Multiscale luminance adjustment-guided fusion for the dehazing of underwater images," Journal of Electronic Imaging 33(1), 013007 (8 January 2024). https://doi.org/10.1117/1.JEI.33.1.013007
Received: 18 July 2023; Accepted: 14 December 2023; Published: 8 January 2024
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KEYWORDS
Image fusion

Image enhancement

Image processing

Color

Visualization

RGB color model

Cameras

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