# Amplifying the imaging power of digital sky surveys with space telescopes data and generative AI

arXiv: https://arxiv.org/abs/2608.20666 · HTML: https://mlsanity.com/paper/2608.20666/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 21 Aug 2026.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Pros and Cons of GAN Evaluation Measures: New Developments](https://mlsanity.com/paper/2103.09396/) (2021)
- [Explaining in Style: Training a GAN to explain a classifier in StyleSpace](https://mlsanity.com/paper/2104.13369/) (2021)
- [Rotation-invariant convolutional neural networks for galaxy morphology prediction](https://mlsanity.com/paper/1503.07077/) (2015)
- [How good is my GAN?](https://mlsanity.com/paper/1807.09499/) (2018)
- [Patch-Based Image Inpainting with Generative Adversarial Networks](https://mlsanity.com/paper/1803.07422/) (2018)
- [Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN](https://mlsanity.com/paper/1706.03319/) (2017)
- [Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks](https://mlsanity.com/paper/1811.03081/) (2018)
- [Rotation Invariant Descriptors for Galaxy Morphological Classification](https://mlsanity.com/paper/1812.04706/) (2018)
- [Automatic identification of outliers in Hubble Space Telescope galaxy images](https://mlsanity.com/paper/2101.02623/) (2021)
- [Galaxy image simplification using Generative AI](https://mlsanity.com/paper/2507.11692/) (2025)

## Abstract (arXiv)

While Digital sky surveys provide excellent throughput of image data and can cover a large footprint, their imaging power is normally inferior to that of space-based telescopes. Space-based telescopes, on the other hand, provide excellent imaging power and can image the deep Universe, but cannot provide the same throughput as advanced ground-based sky surveys. Here, we utilize generative AI to elevate the quality of galaxy images taken by ground-based telescopes to the level of details enabled by space telescopes. The solution is based on the nature of galaxy shapes, allowing generative AI trained on space-based images to convert weak signal into detailed and clear galaxy images. The method allows for combining the high throughput of ground-based sky surveys with the image quality of space-based telescopes. The source code for the method is available, as well as paired training data and a catalog of 63,202 galaxy images enhanced by the proposed method. We also provide a software tool that encapsulates the entire pipeline and the custom generative AI model to generate galaxy images with enhanced quality.