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This is a generic U-Net implementation as proposed by Ronneberger et al. <https://arxiv.org/pdf/1505.04597.pdf>_ developed with Tensorflow. The code has been developed and used for Radio Frequency Interference mitigation using deep convolutional neural networks <http://arxiv.org/abs/1609.09077>_ .
The network can be trained to perform image segmentation on arbitrary imaging data. Checkout the Usage <http://tf-unet.readthedocs.io/en/latest/usage.html>_ section or the included Jupyter notebooks for a toy problem <https://github.com/jakeret/tf_unet/blob/master/demo/demo_toy_problem.ipynb>_ or the Radio Frequency Interference mitigation <https://github.com/jakeret/tf_unet/blob/master/demo/demo_radio_data.ipynb>_ 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.
.. image:: https://raw.githubusercontent.com/jakeret/tf_unet/master/docs/toy_problem.png :alt: Segmentation of a toy problem. :align: center
To more complex application such as the detection of radio frequency interference (RFI) in radio astronomy.
.. image:: https://raw.githubusercontent.com/jakeret/tf_unet/master/docs/rfi.png :alt: Segmentation of RFI in radio data. :align: center
Or to detect galaxies and star in wide field imaging data.
.. image:: https://raw.githubusercontent.com/jakeret/tf_unet/master/docs/galaxies.png :alt: Segmentation of a galaxies. :align: center
