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Astronomical Image Quality Assessment Based on Deep Learning for Resource-constrained Environments

文献类型：期刊论文

作者Li, Juan3,4; Zhang, Xiaoming3,4; Ge, Jiayi3,4; Bai, Chunhai1; Feng, Guojie1; Mu, Haiyang3,4; Wang, Lei3,4; Liu, Chengzhi2,4; Kang, Zhe2,4; Jiang, Xiaojun3,4
刊名PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC
出版日期2025-03-01
卷号137期号:3页码:034502
ISSN号0004-6280
DOI10.1088/1538-3873/adb790
产权排序3
英文摘要This paper presents a highly lightweight model for astronomical image quality assessment, named AQSA-Net, designed to address the challenges of evaluating image quality in scenarios with limited computational resources and rapid decision-making needs. With only 0.15B in computational cost and 0.67M parameters, AQSA-Net significantly reduces memory usage while enhancing inference speed and real-time processing. We construct a data set with eight quality categories based on actual astronomical images. We develop an efficient feature extraction and data processing method that integrates local and global image information, substantially reducing input resolution and training time. We optimize the AQSA-Net architecture and introduce a spatial attention unit, enabling the model to focus on key image areas, enhancing feature extraction while reducing computational overhead. AQSA-Net is compared with several classic deep convolutional neural networks, and experimental results show that AQSA-Net achieves state-of-the-art performance with minimal computational complexity and parameter count. Specifically, AQSA-Net achieves an accuracy of 97.63%, recall of 98.03%, precision of 97.79%, and F1-score of 97.91% on the test set. Additionally, to more accurately assess the quality of usable images, we construct a quantitative image quality factor and a quality grading system, providing quantifiable evaluation criteria for subsequent scientific research. Therefore, our method effectively distinguishes high-quality images from low-quality ones that may impact scientific projects. This provides a reliable automated quality assessment tool for large-scale, complex data sets requiring deep learning inspection. Furthermore, our image quality evaluation could support the assessment of scientific observation data.
WOS关键词SYSTEM
资助项目National Science and Technology Major Projecthttps://doi.org/10.13039/501100018537[2022ZD0117401] ; National Science and Technology Major Project[N87] ; Nanshan one-meter wide-field telescope (NOWT) ; Xinjiang Astronomical Observatory
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001442108900001
出版者IOP Publishing Ltd
资助机构National Science and Technology Major Projecthttps://doi.org/10.13039/501100018537 ; National Science and Technology Major Project ; Nanshan one-meter wide-field telescope (NOWT) ; Xinjiang Astronomical Observatory
源URL[http://ir.xao.ac.cn/handle/45760611-7/7502]  
专题光学天文与技术应用研究室_光学天文技术研究团组
通讯作者Li, Juan; Zhang, Xiaoming; Jiang, Xiaojun
作者单位1.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China
2.Chinese Acad Sci, Changchun Observ, Natl Astron Observ, Changchun 130117, Peoples R China
3.Chinese Acad Sci, Natl Astron Observ, CAS Key Lab Opt Astron, Beijing 100101, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Li, Juan,Zhang, Xiaoming,Ge, Jiayi,et al. Astronomical Image Quality Assessment Based on Deep Learning for Resource-constrained Environments[J]. PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC,2025,137(3):034502.
APA Li, Juan.,Zhang, Xiaoming.,Ge, Jiayi.,Bai, Chunhai.,Feng, Guojie.,...&Jiang, Xiaojun.(2025).Astronomical Image Quality Assessment Based on Deep Learning for Resource-constrained Environments.PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC,137(3),034502.
MLA Li, Juan,et al."Astronomical Image Quality Assessment Based on Deep Learning for Resource-constrained Environments".PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC 137.3(2025):034502.

入库方式： OAI收割

来源：新疆天文台

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