This is a generic U-Net implementation as proposed by Ronneberger et al. developed with Tensorflow. The code has been developed and used for Radio Frequency Interference mitigation using deep convolutional neural networks .
The network can be trained to perform image segmentation on arbitrary imaging data. Checkout the Usage section or the included Jupyter notebooks for a toy problem or the Radio Frequency Interference mitigation discussed in our paper.
The code is not tied to a specific segmentation such that it can be used in a toy problem to detect circles in a noisy image.
To more complex application such as the detection of radio frequency interference (RFI) in radio astronomy.
Or to detect galaxies and star in wide field imaging data.
As you use tf_unet for your exciting discoveries, please cite the paper that describes the package:
@article{akeret2017radio,
title={Radio frequency interference mitigation using deep convolutional neural networks},
author={Akeret, Joel and Chang, Chihway and Lucchi, Aurelien and Refregier, Alexandre},
journal={Astronomy and Computing},
volume={18},
pages={35--39},
year={2017},
publisher={Elsevier}
}