# tf_unet

**Repository Path**: sunmingyang1987/tf_unet

## Basic Information

- **Project Name**: tf_unet
- **Description**: Generic U-Net Tensorflow implementation for image segmentation
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-02-04
- **Last Updated**: 2020-12-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

=============================
Tensorflow Unet
=============================

.. image:: https://readthedocs.org/projects/tf-unet/badge/?version=latest
	:target: http://tf-unet.readthedocs.io/en/latest/?badge=latest
	:alt: Documentation Status
		
.. image:: http://img.shields.io/badge/arXiv-1609.09077-orange.svg?style=flat
        :target: http://arxiv.org/abs/1609.09077

.. image:: https://img.shields.io/badge/ascl-1611.002-blue.svg?colorB=262255
        :target: http://ascl.net/1611.002

.. image:: https://mybinder.org/badge.svg
        :target: https://mybinder.org/v2/gh/jakeret/tf_unet/master?filepath=demo%2Fdemo_toy_problem.ipynb


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


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}
	}