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5 February 2025 Underwater image restoration with UNet model
JunGui Wang, JunWui Hsieh
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Proceedings Volume 13510, International Workshop on Advanced Imaging Technology (IWAIT) 2025; 135101I (2025) https://doi.org/10.1117/12.3058023
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2025, 2025, Douliu City, Taiwan
Abstract
This abstract discusses methods and techniques for underwater image restoration. Underwater images are often affected by factors such as light scattering, color dispersion, and suspended particles, leading to blurriness, distortion, and difficulty in recognizing features. In order to improve the quality of underwater images, researchers have proposed restoration techniques based on mathematical models and computational methods. These include steps such as removal of scattered light, color correction, filtering, and contrast enhancement to enhance the clarity and realism of the images. Additionally, the application of deep learning techniques in underwater image restoration has shown significant progress. Through extensive training with large datasets, models can automatically learn and adapt to the restoration needs of different underwater environments. In summary, this study proposed a Vision Transformer-base UNet model for underwater image restoration which test on Large Scale Underwater Image(LSUI) dataset.
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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JunGui Wang and JunWui Hsieh "Underwater image restoration with UNet model", Proc. SPIE 13510, International Workshop on Advanced Imaging Technology (IWAIT) 2025, 135101I (5 February 2025); https://doi.org/10.1117/12.3058023
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KEYWORDS
Image restoration
Transformers
Data modeling
Image fusion
RGB color model
Education and training
Image enhancement
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