We propose FluoGAN, an unsupervised hybrid approach combining the physical modelling of fluorescence microscopy timelapse acquisitions with a Generative Adversarial Network (GAN) for the problem of image deconvolution. Differently from standard approaches combining a least-square data fitting term based on one (longtime exposure) image with...
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September 28, 2022 (v1)PublicationUploaded on: December 3, 2022
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March 29, 2023 (v1)Publication
We propose FluoGAN, an unsupervised hybrid approach combining the physical modelling of fluorescence microscopy timelapse acquisitions with a Generative Adversarial Network (GAN) for the problem of image deconvolution. Differently from standard approaches combining a least-square data fitting term based on one (longtime exposure) image with...
Uploaded on: April 14, 2023