Denoising of chemical imaging and tomography data

化学成像和断层扫描数据的去噪

• 批准号: 10060435
• 金额: $ 5.17万
• 依托单位: FINDEN LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10060435
• 关键词: Denoising; chemical; imaging; tomography; data

Finden and NPL are working together to develop novel statistical and machine-learning based methods to reduce the noise levels for chemical imaging and tomography datasets. The proposed approaches will result in clearer images with sharper associated spectra/patterns, aiding interpretation and quantification of the data. The approach will be applicable to many different forms of hyperspectral/scattering based characterisation, both chemical mapping and chemical computed tomography (CT) e.g. XRF-CT/mapping, XRD-CT/mapping, IR , as well as having potential benefits to more conventional imaging methods such as X-ray-CT.Successful denoising will allow us to work with weaker signals than before, opening up possibilities for faster measurement times (resulting in cost savings that can be passed on to the customer), resulting in higher throughput of chemical characterisation, but also the option of maintaining image quality but with a reduced X-ray dose - of benefit to medical imaging.

芬登和NPL正在合作开发基于统计和机器学习的新方法，以降低化学成像和断层扫描数据集的噪音水平。拟议的方法将产生更清晰的图像和更清晰的相关光谱/模式，有助于数据的解释和量化。该方法将适用于许多不同形式的基于高光谱/散射的表征，既包括化学测绘和化学计算机断层扫描(CT)，例如XRF-CT/测绘、X射线-CT/测绘、IR，也具有更传统成像方法的潜在优势，例如X射线-CT。成功的去噪将使我们能够处理比以前更弱的信号，打开更快的测量时间的可能性(导致成本节约，可以传递给客户)，从而导致更高的化学表征吞吐量，而且还可以选择保持图像质量但降低X射线剂量-这对医学成像是有益的。