erukude/galaxy-simplifier

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.

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Model Sources

• Code & full project:
  https://github.com/SaiTeja-Erukude/galaxy-image-simplification-using-genai

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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)

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Intended Use

What this model does

Given an optical galaxy image (RGB, 256×256):

1. 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
   • Typical usage: apply a confidence threshold on the spiral class (e.g. p_spiral > 0.65) before running the GAN pipeline.

2. 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).

3. 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

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How to use

You can either:

• use your own inference script, or
• use the provided minimalistic inference.py.

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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}
}