Abstract
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.
Abstract (translated)
在严重退化情况下进行图像重建仍然是一个具有挑战性的逆向问题，特别是在高能物理加速器的束流诊断中。随着现代设施对精确检测束晕结构以控制损失的需求日益增加，传统的分析工具已达到其性能极限。本文回顾了现有的数据清洗、轮廓提取和发射度重建的图像处理技术，并提出了一种基于卷积滤波和优化早期停止策略的神经网络的新方法，以控制过拟合问题。尽管缺乏训练数据集，所提出的无监督框架在低信噪比条件下实现了对束流发射度图像的稳健去噪和高保真重构。该方法将可测量幅度扩展到七个标准差以上，从而实现前所未有的束晕分辨率。
URL
https://arxiv.org/abs/2603.06689
