Applications of Infinite Dimensional Compressive Sensing to Multi-Dimensional Analog Images using Machine Learning to Enhance Results

利用机器学习将无限维压缩感知应用于多维模拟图像以增强结果

• 批准号: 2889834
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889834
• 关键词: Applications; Infinite; Dimensional; Compressive; Sensing

Working with SenseAI, the objective of this project is to develop a STEM imaging system based on infinite-dimensional CS that optimises a sampling strategy involving a continuous probe position domain as opposed to the current finite methods where the locations of the probes are a priori fixed while the recovery algorithm maps subsampled data to an analog image with low computational complexity. Such a capability would significantly increase the key metrics of resolution, precision and sensitivity, providing an increased capability for STEM to deliver unique scientific results. As many scientific methods generate images in similar manners, this approach will have a wide impact.STEM imaging consists of a focused electron beam (or probe) scanning over a thin sample, while a range of different scattering signals are simultaneously collected (creating a time resolved hyperspectral dataset). Low-dose and fast STEM imaging is now a reality thanks to the application of Compressive Sensing (CS) to all imaging modes in the microscope. Specifically, in STEM compressive sensing allows the instrument to sub-sample the probe positions at rates dramatically lower than the Shannon-Nyquist sampling rate, provided that the target image has a sparse representation in a dictionary or basis, e.g., Discrete Cosine Transform. However, while this methodology has been shown to work, the existing CS STEM frameworks are based on finite-dimensional CS: they concern the recovery of a discrete (or pixelated) image. The issue limiting the power of these reconstructions to generate scientific insights at the moment is that STEM images are in fact analog (or continuous-space) and the application of finite-dimensional CS can lead to artefacts in the reconstruction that, in some cases, makes it difficult to distinguish the real features. The images may also in some cases not be sparse in a basis but possess an asymptotic sparsity, and therefore, the experimental sampling strategy of probe positions needs be to take account of these two factors.Initial applications of this technology have focused on electron microscopy, where a real time acquisition mode for atomic resolution images/spectroscopy has been developed in which inpainting reconstructions are aided by a critical deep learning step - the microscopes are learning how to take the best images for themselves and then optimising the experimental acquisition. SenseAI is now working with several major instrument manufacturers to broaden these new approaches to instruments using X-rays, ions, neutrons and optics in addition to the existing portfolio of electron microscopes, with the goal of developing self-driving acquisition and analysis capabilities in the near future.

该项目的目标是与 SenseAI 合作开发一种基于无限维 CS 的 STEM 成像系统，该系统优化涉及连续探头位置域的采样策略，而不是当前探头位置先验固定的有限方法而恢复算法将子采样数据映射到计算复杂度较低的模拟图像。这种能力将显着提高分辨率、精度和灵敏度等关键指标，从而增强 STEM 交付独特科学成果的能力。由于许多科学方法以类似的方式生成图像，因此这种方法将产生广泛的影响。STEM 成像由聚焦电子束（或探针）在薄样品上扫描组成，同时收集一系列不同的散射信号（创建时间解析的高光谱数据集）。由于压缩传感 (CS) 在显微镜中所有成像模式的应用，低剂量和快速 STEM 成像现已成为现实。具体来说，在 STEM 压缩传感中，只要目标图像在字典或基础中具有稀疏表示（例如离散余弦变换），仪器就可以以远低于香农-奈奎斯特采样率的速率对探针位置进行二次采样。然而，虽然这种方法已被证明有效，但现有的 CS STEM 框架基于有限维 CS：它们涉及离散（或像素化）图像的恢复。目前限制这些重建产生科学见解的能力的问题是，STEM 图像实际上是模拟的（或连续空间），并且有限维 CS 的应用可能会导致重建中出现伪影，在某些情况下，使得很难区分真实特征。在某些情况下，图像也可能在基础上不是稀疏的，而是具有渐近稀疏性，因此，探针位置的实验采样策略需要考虑这两个因素。该技术的最初应用集中在电子显微镜、其中开发了原子分辨率图像/光谱学的实时采集模式，其中修复重建由关键的深度学习步骤辅助 - 显微镜正在学习如何为自己拍摄最佳图像，然后优化实验采集。 SenseAI 目前正与几家主要仪器制造商合作，除了现有的电子显微镜产品组合之外，还将这些新方法扩展到使用 X 射线、离子、中子和光学的仪器，目标是开发自动驾驶采集和分析能力。不久的将来。