Kavli Affiliate: Xiang Zhang
| First 5 Authors: Kuo-Cheng Wu, Kuo-Cheng Wu, , ,
| Summary:
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.
Leveraging this benchmark, we propose a Flux-Invariant Super Resolution (FISR)
model that could accurately infer the flux-consistent high-resolution images
from input photometry, suppressing several SR state-of-the-art methods by
24.84% on a novel designed flux consistency metric, showing the priority of our
method for astrophysics. Extensive experiments demonstrate the effectiveness of
our proposed method and the value of our dataset. Code and models are available
at https://github.com/GuoCheng12/STAR.
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