Institutional Repository of Optical Astronomy Research Laboratory
| Astronomical Image Quality Assessment Based on Deep Learning for Resource-constrained Environments | |
Li, Juan1,2; Zhang, Xiaoming1,2; Ge, Jiayi1,2; Bai, Chunhai3 ; Feng, Guojie3; Mu, Haiyang1,2; Wang, Lei1,2; Liu, Chengzhi2,4; Kang, Zhe2,4; Jiang, Xiaojun1,2
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| 2025-03-01 | |
| Source Publication | PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC
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| ISSN | 0004-6280 |
| Volume | 137Issue:3Pages:034502 |
| Contribution Rank | 3 |
| Abstract | 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. |
| DOI | 10.1088/1538-3873/adb790 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | SYSTEM |
| Funding Project | 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 Research Area | Astronomy & Astrophysics |
| WOS Subject | Astronomy & Astrophysics |
| WOS ID | WOS:001442108900001 |
| Publisher | IOP Publishing Ltd |
| Funding Organization | 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 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/7503 |
| Collection | 光学天文与技术应用研究室_光学天文技术研究团组 |
| Corresponding Author | Li, Juan; Zhang, Xiaoming; Jiang, Xiaojun |
| Affiliation | 1.Chinese Acad Sci, Natl Astron Observ, CAS Key Lab Opt Astron, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China 4.Chinese Acad Sci, Changchun Observ, Natl Astron Observ, Changchun 130117, Peoples R China |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Li-2025-Astronomical（1348KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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