无监督学习提升粒子加速器束流图像重建精度，突破噪声干扰极限。
High-Resolution Image Reconstruction with Unsupervised Learning and Noisy Data Applied to Ion-Beam Dynamics for Particle Accelerators
- 采用卷积滤波与神经网络结合的无监督框架，用早期停止防过拟合。
- 在低信噪比下实现高保真束流发射度图像重建，可测幅度超7个标准差。
- 适合高能物理束流诊断，尤其适用于缺乏标注数据的场景。
在严重退化条件下进行图像重建仍是极具挑战性的逆问题，尤其是在高能物理加速器的束流诊断中。随着现代设施对束流晕结构精确检测以控制损失的需求增加，传统分析工具已逼近性能极限。本文综述了现有图像处理技术在数据清洗、轮廓提取和发射度重建中的应用，并提出一种基于卷积滤波与神经网络的新型方法，结合优化的早期停止策略以控制过拟合。尽管缺乏训练数据，该无监督框架仍能在低信噪比条件下实现鲁棒去噪与高保真束流发射度图像重建，将可测量幅度扩展至七倍以上标准差，实现前所未有的晕结构分辨率。
原文摘要 · Abstract (English)
Image reconstruction in the presence of severe degradation remains a challenging inverse problem, particularly in beam diagnostics for high-energy physics accelerators. As modern facilities demand precise detection of beam halo structures to control losses, traditional analysis tools have reached their performance limits. This work reviews existing image-processing techniques for data cleaning, contour extraction, and emittance reconstruction, and introduces a novel approach based on convolutional filtering and neural networks with optimized early-stopping strategies in order to control overfitting. Despite the absence of training datasets, the proposed unsupervised framework achieves robust denoising and high-fidelity reconstruction of beam emittance images under low signal-to-noise conditions. The method extends measurable amplitudes beyond seven standard deviations, enabling unprecedented halo resolution.
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