mlsanityData to 14 Oct 2026

Galaxy image simplification using Generative AI

arXiv 2507.11692 ↗First posted 15 Jul 2025astro-ph.GA astro-ph.IM cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Modern digital sky surveys have been acquiring images of billions of galaxies. While these images often provide sufficient details to analyze the shape of the galaxies, accurate analysis of such high volumes of images requires effective automation. Current solutions often rely on machine learning annotation of the galaxy images based on a set of pre-defined classes. Here we introduce a new approach to galaxy image analysis that is based on generative AI. The method simplifies the galaxy images and automatically converts them into a ``skeletonized" form. The simplified images allow accurate measurements of the galaxy shapes and analysis that is not limited to a certain pre-defined set of classes. We demonstrate the method by applying it to galaxy images acquired by the DESI Legacy Survey. The code and data are publicly available. The method was applied to 125,000 DESI Legacy Survey images, and the catalog of the simplified images is publicly available.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 72 references that are in the map. First posted on arXiv 15 Jul 2025.

Builds on

19 of 72 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  5. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 123
  6. 2021Explaining in Style: Training a GAN to explain a classifier in StyleSpacecited by 122
  7. 2015Rotation-invariant convolutional neural networks for galaxy morphology predictioncited by 83
  8. 2018How good is my GAN?cited by 74
  9. 2018Patch-Based Image Inpainting with Generative Adversarial Networkscited by 70
  10. 2021Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxiescited by 41
  11. 2017Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GANcited by 28
  12. 2024Identifying Bias in Deep Neural Networks Using Image Transformscited by 6
7 more
  1. 2014Combining human and machine learning for morphological analysis of galaxy imagescited by 5
  2. 2020A catalog of broad morphology of Pan-STARRS galaxies based on deep learningcited by 5
  3. 2022Systematic biases when using deep neural networks for annotating large catalogs of astronomical imagescited by 5
  4. 2018Rotation Invariant Descriptors for Galaxy Morphological Classificationcited by 4
  5. 2021Automatic identification of outliers in Hubble Space Telescope galaxy imagescited by 3
  6. 2022Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label Smoothingcited by 1
  7. 2023Outlier galaxy images in the Dark Energy Survey and their identification with unsupervised machine learningcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Amplifying the imaging power of digital sky surveys with space telescopes data and generative AIcited by 0