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Cutting-edge deep learning techniques for bioimage analysis
DL4MicEverywhere / wgan-zerocostdl4mic / 1.15.1

wgan-zerocostdl4mic implementation.

Super-resolution via super-pixelisation. Wasserstein GAN (DFCAN) is a network created to transform low-resolution (LR) images to super-resolved (SR) images, published by Gulrajani I. et al. arXiv 2017. The training is done using LR-SR image pairs, taking the LR images as input and obtaining an output as close to SR as posible.
Tags
AMD64colabnotebookdenoisingZeroCostDL4Mic
Citation
https://doi.org/10.1038/s41467-021-22518-0von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0
https://doi.org/10.48550/arXiv.1704.00028Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V. and Courville, A. Improved Training of Wasserstein GANs. arXiv. 2017 doi: https://doi.org/10.48550/arXiv.1704.00028.
Solution written by
DL4MicEverywhere team
album team

Arguments

--path
What is your working path? (default value: .)

Usage instructions

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