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- W4205919498 abstract "Understanding the underwater environment is important for navigation, surveillance, and exploration. The aim is to restore underwater images degraded by light absorption and scattering in water medium resulting in poor visibility, low brightness, and low contrast of the scene. In this paper, we introduce a network based on the image to image translation that is Contextual Conditional Generative Adversarial Network (CCGAN) and compare its performance with the available image to image translation models namely, Unsupervised Image Translation (UNIT), cycleGAN, Contrastive Unpaired Translation (CUT) and fastCUT qualitatively and quantitatively. Both supervised and unsupervised models are analyzed and we show that the proposed model outrun the alternatives." @default.
- W4205919498 created "2022-01-25" @default.
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- W4205919498 date "2021-10-26" @default.
- W4205919498 modified "2023-09-27" @default.
- W4205919498 title "Comparative Study of Image to Image Translation Models for Underwater Image Enhancement" @default.
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- W4205919498 doi "https://doi.org/10.1109/icrai54018.2021.9651466" @default.
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