Underwater Image Enhancement Based on Light Field-Guided Rendering Network

Underwater images often encounter challenges such as attenuation, color distortion, and noise caused by artificial lighting sources. These imperfections not only degrade image quality but also impose constraints on related application tasks. Improving underwater image quality is crucial for underwat...

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Bibliographic Details
Published in:Journal of marine science and engineering 2024-07, Vol.12 (7), p.1217
Main Authors: Yeh, Chia-Hung, Lai, Yu-Wei, Lin, Yu-Yang, Chen, Mei-Juan, Wang, Chua-Chin
Format: Article
Language:English
Subjects:
Algorithms
Color
colorization
convolutional neural network
Deep learning
Image degradation
Image enhancement
Image processing
Image quality
Image restoration
Light
light field
Methods
Modules
Rendering
rendering network
restoration
Underwater
Underwater exploration
underwater image enhancement
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Summary:Underwater images often encounter challenges such as attenuation, color distortion, and noise caused by artificial lighting sources. These imperfections not only degrade image quality but also impose constraints on related application tasks. Improving underwater image quality is crucial for underwater activities. However, obtaining clear underwater images has been a challenge, because scattering and blur hinder the rendering of true underwater colors, affecting the accuracy of underwater exploration. Therefore, this paper proposes a new deep network model for single underwater image enhancement. More specifically, our framework includes a light field module (LFM) and sketch module, aiming at the generation of a light field map of the target image for improving the color representation and preserving the details of the original image by providing contour information. The restored underwater image is gradually enhanced, guided by the light field map. The experimental results show the better image restoration effectiveness, both quantitatively and qualitatively, of the proposed method with a lower (or comparable) computing cost, compared with the state-of-the-art approaches.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse12071217