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.
