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- W4377237655 abstract "In the last years, several deep learning models, especially generative adversarial networks (GAN) have received a lot of attention to perform the task of single image super-resolution (SISR). These methods build up an end-to-end framework, which shows super resolution (SR) for a given low resolution (LR) image in a single step and achieves state-of-the-art performance. This paper develops an application which can perform super-resolution microscopy in real-time in a standard GPU. In this work, we propose a speed up in training process of the network. Furthermore, we suggest changes to an existing super resolution network to improve the results. We then explore the possibility of the trained network to produce high-resolution HR outputs for different domains. Finally, we evaluate our model on various images and videos to show that it performs better than the baseline network and other approaches and can perform inference in real time, thus opening its applications in different areas like low-end benchtop and even mobile microscopy." @default.
- W4377237655 created "2023-05-23" @default.
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- W4377237655 date "2023-01-01" @default.
- W4377237655 modified "2023-10-01" @default.
- W4377237655 title "Attaining Real-Time for Microscopic Images Using GAN" @default.
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- W4377237655 doi "https://doi.org/10.1007/978-981-99-0969-8_55" @default.
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