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

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ID: 325969
2026
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Ranked #385 of 1,339 articles by views in monthly notices of the royal astronomical society

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Abstract
Abstract 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.
Reference Key
openalex_W7204004347 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sai Teja Erukude, Lior Shamir
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Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag1594
URL
https://doi.org/10.1093/mnras/stag1594
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