Star reduction in deep sky images

Starrem2k13 is a simple tool for removing stars from astronomical images. Starrem2k13 uses a GAN trained on augmented data. It's code was inspired from a sample at Tensorflow's website. The training data consists of only three base images.

Below are examples of what it can do:

Installing

Currently binaries for Windows, Linux and MacOS are available in the releases section. These were created using PyInstaller. Using pre-complied binary is the recommended way of running Starrem2k13. Once you have downloaded and installed the program you can run it simply by typing the following in command prompt/terminal

starrem2k13 image_with_stars.jpg  image_without_stars.jpg

Running using Docker (if binaries don't work)

docker run   -v $PWD:/usr/src/app/starrem2k13/data  \
-it code2k13/starrem2k13 \
/bin/bash -c "./starrem2k13.py ./data/example.jpg  ./data/example_starless.jpg"

Note that $PWD refers to your current working directory. In the above example it is assumed that the file example.jpg resides in your current working directory. This directory is mounted as a volume with the path /usr/src/app/starrem2k13/data inside the docker container. The output image example_starless.jpg will also be written to same directory.

Running with local python installation (for geeks)

Clone the repository and navigate to the 'starrem2k13' folder. Install required packages :

git clone https://github.com/code2k13/starrem2k13.git
cd starrem2k13
pip install -r requirements.txt

Additionally you may also have to install lfs support for git

sudo apt-get install git-lfs
git lfs pull

📍If you run into bandwidth issues with git-lfs, you can simply download weights from here and unzip to root directory (a folder with weights will get created beside starrem2k13 executable. This folder contains weights required by the program).

Run inference on image.

python starrem2k13.py image_with_stars.jpg  image_without_stars.jpg

Supprots greyscale and RGB images. Alpha channel (if any) in the source image is removed during processing. Gives issues on some types of TIFF files.

Trying out the model in a web browser

Here is link to a online demo of star reduction created using a trained model, TFJS and ReactJS. Please use a desktop browser to access the demo (for memory and performance reasons). The demo runs locally inside your browser, no data outside of your computer. Here is the link to the demo : https://ashishware.com/static/star_removal/index.html

Training model on your images

The notebook is available in the train folder.

You can also view/run it on Kaggle: https://www.kaggle.com/finalepoch/star-removal-from-astronomical-images-with-pix2pix

Attribution

The training images used in this code were sourced from Wikimedia Commons and processed using GIMP.

Star cluster NGC 3572 and its surroundings

This image was downloaded from Wikimedia Commons and star mask was created by me using GIMP

Link to the processed image: training_data/star_map_base.png

ESO/G. Beccari, CC BY 4.0, via Wikimedia Commons

Url: https://commons.wikimedia.org/wiki/File:The_star_cluster_NGC_3572_and_its_dramatic_surroundings.jpg

Direct Link: https://upload.wikimedia.org/wikipedia/commons/9/95/The_star_cluster_NGC_3572_and_its_dramatic_surroundings.jpg