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• W2969623892 abstract "Because of the optical properties of medical fluorescence images (FIs) and hardware limitations, light scattering and diffraction constrain the image quality and resolution. In contrast to device-based approaches, we developed a post-processing method for FI resolution enhancement by employing improved generative adversarial networks. To overcome the drawback of fake texture generation, we proposed total gradient loss for network training. Fine-tuning training procedure was applied to further improve the network architecture. Finally, a more agreeable network for resolution enhancement was applied to actual FIs to produce sharper and clearer boundaries than in the original images." @default.
• W2969623892 created "2019-08-29" @default.
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• W2969623892 date "2019-08-22" @default.
• W2969623892 modified "2023-10-16" @default.
• W2969623892 title "Improved generative adversarial networks using the total gradient loss for the resolution enhancement of fluorescence images" @default.
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• W2969623892 doi "https://doi.org/10.1364/boe.10.004742" @default.
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