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- W2782796222 abstract "Underwater optical images are usually influenced by low lighting, high turbidity scattering and wavelength absorption. To solve these issues, a great deal of work has been performed to improve the quality of underwater images. Most of them use the high-intensity LEDs for lighting to obtain the high contrast images. However, in high turbidity water, high-intensity LEDs cause strong scattering and absorption. In this paper, we propose a light field imaging approach for solving underwater imaging problems in a low-intensity light environment. As a solution, we tackle the problem of de-scattering from light field images by using deep convolutional neural networks with depth estimation. Furthermore, a spectral characteristic-based color correction method is used for recovering the color reduction. Experimental results show the effectiveness of the proposed method by challenging real-world underwater imaging." @default.
- W2782796222 created "2018-01-26" @default.
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- W2782796222 date "2018-05-01" @default.
- W2782796222 modified "2023-10-15" @default.
- W2782796222 title "Low illumination underwater light field images reconstruction using deep convolutional neural networks" @default.
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- W2782796222 doi "https://doi.org/10.1016/j.future.2018.01.001" @default.
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