Galaxy Image Simplification using Generative AI
This repository hosts the pretrained models for Galaxy Image Simplification using Generative AI, a pipeline that converts complex galaxy images into simplified, skeletonized representations suitable for quantitative morphology analysis.
The pipeline combines:
- A ResNet-based classifier to select spiral galaxies
- A conditional GAN (cGAN) to produce initial arm masks
- A post-processing cGAN to smooth and connect broken arm segments
These models were trained on images from the DESI Legacy Survey with manually annotated spiral arms.
Model Sources
- Code & full project:
https://github.com/SaiTeja-Erukude/galaxy-image-simplification-using-genai
Files in this repository
| File name | Type | Description |
|---|---|---|
models/galaxy_classifier_resnet50.h5 | Keras model | ResNet-based binary classifier: spiral vs. non-spiral galaxy |
models/galaxy_simplifier_cgan.h5 | Keras model | Conditional GAN: galaxy RGB image ➜ initial arm-highlighted image |
models/postprocess_cgan.h5 | Keras model | Conditional GAN: initial mask ➜ refined, smooth/connected mask |
predict.py | Python script | Full inference pipeline (classification ➜ simplifier cGAN ➜ post-cGAN) |
graphical_abstract.jpg | Image | Graphical abstract / high-level overview of the Galaxy Simplifier pipeline |
requirements.txt | Text file | Python dependencies needed for running inference |
README.md | Markdown | Model card and usage instructions (this file) |
Intended Use
What this model does
Given an optical galaxy image (RGB, 256×256):
-
ResNet classifier (
galaxy_classifier_resnet50.h5)- Predicts whether the galaxy is a spiral.
- Outputs a 2-class softmax:
- class
0– non-spiral / other - class
1– spiral
- class
- Typical usage: apply a confidence threshold on the spiral class (e.g.
p_spiral > 0.65) before running the GAN pipeline.
-
Skeletonization cGAN (
galaxy_simplifier_cgan.h5)- Input: original RGB galaxy image (normalized to
[-1, 1]). - Output: image where white lines track the spiral arms (initial skeleton-like mask).
- Input: original RGB galaxy image (normalized to
-
Post-processing cGAN (
postprocess_cgan.h5)- Input: initial cGAN output.
- Output: refined mask with smoother and better-connected arm structures.
- This can be further processed with classical image processing (thresholding, skeletonization, dilation) to produce final binary masks.
Primary use cases
- Large-scale spiral galaxy selection and morphology analysis
- Measuring arm geometry, pitch angles, and other structural properties
- Building catalogs of simplified galaxy images from wide-field surveys
Not intended for
- General-purpose image generation outside the astronomy domain
- High-fidelity photometric modeling or pixel-perfect reconstruction of galaxies
How to use
You can either:
- use your own inference script, or
- use the provided minimalistic
inference.py.
Citation
If you use this code, models, or catalog in your research, please cite:
@article{erukude2025galaxy,
title={Galaxy image simplification using Generative AI},
author={Erukude, Sai Teja and Shamir, Lior},
journal={Astronomy and Computing},
pages={100990},
year={2025},
publisher={Elsevier}
}