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---
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language: en
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license: mit
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tags:
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- gan
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- cgan
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- keras
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- tensorflow
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- computer-vision
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- image-processing
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- astronomy
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- galaxy-morphology
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- image-segmentation
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pipeline_tag: image-to-image
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library_name: keras
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datasets:
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- desi-legacy-survey
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---
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# Galaxy Image Simplification using Generative AI
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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.
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The pipeline combines:
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- A **ResNet-based classifier** to select **spiral galaxies**
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- A **conditional GAN (cGAN)** to produce initial arm masks
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- A **post-processing cGAN** to smooth and connect broken arm segments
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These models were trained on images from the **DESI Legacy Survey** with manually annotated spiral arms.
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<p align="center">
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<img src="graphical_abstract.jpg" width="500">
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</p>
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---
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## Model Sources
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- **Code & full project:**
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https://github.com/SaiTeja-Erukude/galaxy-image-simplification-using-genai
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---
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## Files in this repository
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| File name | Type | Description |
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|----------------------------------|---------------|-------------------------------------------------------------------|
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| `models/galaxy_classifier_resnet50.h5` | Keras model | ResNet-based binary classifier: spiral vs. non-spiral galaxy |
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| `models/galaxy_simplifier_cgan.h5` | Keras model | Conditional GAN: galaxy RGB image ➜ initial arm-highlighted image |
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| `models/postprocess_cgan.h5` | Keras model | Conditional GAN: initial mask ➜ refined, smooth/connected mask |
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| `predict.py` | Python script | Full inference pipeline (classification ➜ simplifier cGAN ➜ post-cGAN) |
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| `graphical_abstract.jpg` | Image | Graphical abstract / high-level overview of the Galaxy Simplifier pipeline |
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| `requirements.txt` | Text file | Python dependencies needed for running inference |
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| `README.md` | Markdown | Model card and usage instructions (this file) |
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---
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## Intended Use
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### What this model does
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Given an optical galaxy image (RGB, 256×256):
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1. **ResNet classifier (`galaxy_classifier_resnet50.h5`)**
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- Predicts whether the galaxy is a **spiral**.
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- Outputs a 2-class softmax:
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- class `0` – non-spiral / other
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- class `1` – spiral
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- Typical usage: apply a confidence threshold on the spiral class (e.g. `p_spiral > 0.65`) before running the GAN pipeline.
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2. **Skeletonization cGAN (`galaxy_simplifier_cgan.h5`)**
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- Input: original RGB galaxy image (normalized to `[-1, 1]`).
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- Output: image where **white lines** track the spiral arms (initial skeleton-like mask).
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3. **Post-processing cGAN (`postprocess_cgan.h5`)**
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- Input: initial cGAN output.
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- Output: refined mask with **smoother and better-connected arm structures**.
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- This can be further processed with classical image processing (thresholding, skeletonization, dilation) to produce final binary masks.
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### Primary use cases
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- Large-scale **spiral galaxy selection** and morphology analysis
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- Measuring arm geometry, pitch angles, and other structural properties
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- Building catalogs of simplified galaxy images from wide-field surveys
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### Not intended for
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- General-purpose image generation outside the astronomy domain
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- High-fidelity photometric modeling or pixel-perfect reconstruction of galaxies
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---
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## How to use
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You can either:
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- use your **own inference script**, or
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- use the provided minimalistic `inference.py`.
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---
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## Citation
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If you use this code, models, or catalog in your research, please cite:
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```bibtex
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@article{erukude2025galaxy,
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title={Galaxy image simplification using Generative AI},
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author={Erukude, Sai Teja and Shamir, Lior},
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journal={Astronomy and Computing},
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pages={100990},
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year={2025},
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publisher={Elsevier}
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}
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``` |
