Denoising of chemical imaging and tomography data

Finden和NPL正在共同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声水平。所提出的方法将产生更清晰的图像，具有更清晰的相关光谱/图案，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，包括化学映射和化学计算机断层扫描（CT），例如XRF-CT/映射、XRD-CT/映射、IR，以及对更传统的成像方法（例如X射线CT）具有潜在的益处。成功的去噪将允许我们处理比以前更弱的信号，为更快的测量时间开辟了可能性（从而节省了成本，并可以转嫁给客户），从而提高了化学表征的吞吐量，而且还可以选择保持图像质量，但减少了X射线剂量-这对医学成像有益。

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.