Denoising of chemical imaging and 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.