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- W4367016330 abstract "Due to the absorption and scattering of light in water, the underwater image has some problems such as colour distortion, serious colour difference and ambiguity, which seriously affects the detection of underwater resources and object recognition. At present, the latest underwater image enhancement methods are all based on deep learning, but they do not take into account the diversity of channel information and pixel features. In order to achieve better enhancement effect, a parallel attention mechanism combining Channel Attention and Pixel Attention is introduced based on Conditional Generative Adversarial Network consisting of multi-scale generator and double discriminator. Compared with other latest methods, the enhancement effect of the proposed method is closer to the real image, and the necessity of each module of the proposed model is proved by ablation study." @default.
- W4367016330 created "2023-04-27" @default.
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- W4367016330 date "2023-02-24" @default.
- W4367016330 modified "2023-10-01" @default.
- W4367016330 title "Underwater Image Enhancement Method Based on CGAN and Parallel Attention Mechanism" @default.
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- W4367016330 doi "https://doi.org/10.1109/nnice58320.2023.10105760" @default.
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