STAR: A Benchmark for Astronomical Star Fields Super-Resolution
Kuo-Cheng Wu, Guohang Zhuang, Jinyang Huang, Xiang Zhang, Wanli Ouyang, Yan Lu

TL;DR
STAR introduces a large, diverse, flux-consistent astronomical super-resolution dataset and a novel flux-invariant SR model, advancing high-resolution astronomical imaging and model evaluation.
Contribution
The paper presents the STAR dataset with flux-preserving image pairs and a new flux error metric, enabling systematic development and evaluation of astrophysical super-resolution models.
Findings
STAR dataset contains 54,738 flux-consistent star field image pairs.
The proposed FISR model outperforms state-of-the-art methods by 24.84% on flux consistency.
Extensive experiments validate the effectiveness of the dataset and the FISR model.
Abstract
Super-resolution (SR) advances astronomical imaging by enabling cost-effective high-resolution capture, crucial for detecting faraway celestial objects and precise structural analysis. However, existing datasets for astronomical SR (ASR) exhibit three critical limitations: flux inconsistency, object-crop setting, and insufficient data diversity, significantly impeding ASR development. We propose STAR, a large-scale astronomical SR dataset containing 54,738 flux-consistent star field image pairs covering wide celestial regions. These pairs combine Hubble Space Telescope high-resolution observations with physically faithful low-resolution counterparts generated through a flux-preserving data generation pipeline, enabling systematic development of field-level ASR models. To further empower the ASR community, STAR provides a novel Flux Error (FE) to evaluate SR models in physical view.…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Stellar, planetary, and galactic studies · Adaptive optics and wavefront sensing
