Radio Frequency Interference mitigation using deep convolutional neural networks

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Ada Coda
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tf_unet: Generic convolutional neural network U-Net implementation in Tensorflow

Post by Ada Coda » Thu Sep 29, 2016 8:28 am

tf_unet: Generic convolutional neural network U-Net implementation in Tensorflow

Abstract: tf_unet mitigates radio frequency interference (RFI) signals in radio data using a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. The code is not tied to a specific segmentation and can be used, for example, to detect radio frequency interference (RFI) in radio astronomy or galaxies and stars in widefield imaging data. This U-Net implementation can outperform classical RFI mitigation algorithms.

Credit: Akeret, Joel; Chang, Chihway; Lucchi, Aurelien; Refregier, Alexandre

Site: https://github.com/jakeret/tf_unet
https://ui.adsabs.harvard.edu/abs/2017A%26C....18...35A

Bibcode: 2016ascl.soft11002A

Preferred citation method: https://ui.adsabs.harvard.edu/abs/2017A%26C....18...35A

ID: ascl:1611.002
Last edited by Ada Coda on Thu Mar 12, 2020 5:16 pm, edited 1 time in total.
Reason: Updated code entry.

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