Astro-L3C: boosting lossless solar image compression with Kolmogorov-Arnold-guided learning
文献类型：期刊论文
| 作者 | Wu, Shichao1; Liu, Yingbo1; Deng, Li2; Ma, Xuan1; Yang L(杨磊)3 |
| 刊名 | MACHINE LEARNING-SCIENCE AND TECHNOLOGY
 |
| 出版日期 | 2026-06-01 |
| 卷号 | 7期号:3 |
| 关键词 | lossless compression Astro-L3C dynamic tanh fisher-based regularization KAN |
| DOI | 10.1088/2632-2153/ae696c |
| 产权排序 | 第3完成单位 |
| 文献子类 | Article |
| 英文摘要 | Rapid advances in astronomical observation technology are generating data volumes that increasingly strain existing storage and transmission infrastructure. Solar observation data poses a particular challenge due to its complex spatiotemporal correlations and multi-scale structures, requiring strict lossless compression to preserve scientific integrity. Conventional compression methods fail to capture the highly nonlinear intensity distributions arising from diverse solar phenomena, and varying observation conditions further increase the risk of overfitting to specific data patterns. To address these challenges, we present Astro-L3C, a framework that advances learned lossless compression (L3C) through a fast Kolmogorov-Arnold network, a dynamic Tanh-based ResBlock, and Fisher information regularization, which jointly enable expressive nonlinear probability modeling, stable feature extraction, and robust generalization across diverse solar phenomena and observing conditions. Experimental evaluation on new vacuum solar telescope data demonstrates that Astro-L3C reduces bits-per-subpixel (bpsp) by 4.6% compared with standard L3C. Benchmarked against established techniques, our method achieves bpsp reductions of 43.84% and 74.30% relative to super-Resolution based Compression and integer discrete flow respectively, confirming consistent improvements for solar observation data compression. This framework provides a new pathway for lossless compression of high-volume solar observation data. |
| 学科主题 | 天文学 ; 天文学其他学科 ; 计算机科学技术 ; 计算机应用 ; 计算机图象处理 |
| URL标识 | 查看原文 |
| 出版地 | No.2 The Distillery, Glassfields, Avon Street, Bristol, ENGLAND |
| 资助项目 | National Natural Science Foundation of China[62262068]; National Natural Science Foundation of China[62462064]; Yunnan University of Finance and Economics Postgraduate Innovation Foundation[2025YUFEYC018]; Yunnan University of Finance and Economics Postgraduate Innovation Foundation[2025YUFEYC116] |
| WOS研究方向 | Computer Science ; Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:001768822100001 |
| 出版者 | IOP Publishing Ltd |
| 资助机构 | National Natural Science Foundation of China[62262068, 62462064] ; Yunnan University of Finance and Economics Postgraduate Innovation Foundation[2025YUFEYC018, 2025YUFEYC116] |
| 版本 | 出版稿 |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/29224]  |
| 专题 | 云南天文台_抚仙湖太阳观测站 |
| 通讯作者 | Liu, Yingbo |
| 作者单位 | 1.School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, People’s Republic of China; 2.School of Mathematics and Statistics, Shaoguan University, Shaoguan, People’s Republic of China; 3.Yunnan Observatories, Chinese Academy of Sciences, Kunming, People’s Republic of China |
| 推荐引用方式 GB/T 7714 | Wu, Shichao,Liu, Yingbo,Deng, Li,et al. Astro-L3C: boosting lossless solar image compression with Kolmogorov-Arnold-guided learning[J]. MACHINE LEARNING-SCIENCE AND TECHNOLOGY,2026,7(3). |
| APA | Wu, Shichao,Liu, Yingbo,Deng, Li,Ma, Xuan,&杨磊.(2026).Astro-L3C: boosting lossless solar image compression with Kolmogorov-Arnold-guided learning.MACHINE LEARNING-SCIENCE AND TECHNOLOGY,7(3). |
| MLA | Wu, Shichao,et al."Astro-L3C: boosting lossless solar image compression with Kolmogorov-Arnold-guided learning".MACHINE LEARNING-SCIENCE AND TECHNOLOGY 7.3(2026). |
入库方式： OAI收割
来源：云南天文台
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。