用深度先验修复地面天文多帧模糊图像，提升星空清晰度。
AstroClearNet: Deep image prior for multi-frame astronomical image restoration
- 基于深度图像先验的自监督多帧联合处理方法
- 在超广角相机数据上实现更锐利的复原效果
- 适合需要高保真天文图像的研究者使用
从模糊观测中恢复夜空的高保真图像是天文学中的基础问题，传统方法常难以应对。在地基天文观测中，通过叠加多帧曝光以提高信噪比时，大气湍流导致的点扩散函数变化进一步增加难度。本文提出一种基于深度图像先验的自监督多帧方法，用于去噪、去模糊和融合地面曝光图像。核心是一个精心设计的卷积神经网络，可整合多帧信息并施加物理合理约束。我们通过处理超广角相机（Hyper Suprime-Cam）的数据验证了该方法的有效性，初步结果表明复原图像更加清晰。
原文摘要 · Abstract (English)
Recovering high-fidelity images of the night sky from blurred observations is a fundamental problem in astronomy, where traditional methods typically fall short. In ground-based astronomy, combining multiple exposures to enhance signal-to-noise ratios is further complicated by variations in the point-spread function caused by atmospheric turbulence. In this work, we present a self-supervised multi-frame method, based on deep image priors, for denoising, deblurring, and coadding ground-based exposures. Central to our approach is a carefully designed convolutional neural network that integrates information across multiple observations and enforces physically motivated constraints. We demonstrate the method's potential by processing Hyper Suprime-Cam exposures, yielding promising preliminary results with sharper restored images.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。