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Noise is an important issue for radiographic and tomographic imagingtechniques. It becomes particularly critical in applications where additionalconstraints force a strong reduction of the Signal-to-Noise Ratio (SNR) perimage. These constraints may result from limitations on the maximum availableflux or permissible dose and the associated restriction on exposure time.Often, a high SNR per image is traded for the ability to distribute a giventotal exposure capacity per pixel over multiple channels, thus obtainingadditional information about the object by the same total exposure time. Thesecan be energy channels in the case of spectroscopic imaging or time channels inthe case of time-resolved imaging. In this paper, we report on a method forimproving the quality of noisy multi-channel (time or energy-resolved) imagingdatasets. The method relies on the recent Noise2Noise (N2N) self-superviseddenoising approach that learns to predict a noise-free signal without access tonoise-free data. N2N in turn requires drawing pairs of samples from a datadistribution sharing identical signals while being exposed to different samplesof random noise. The method is applicable if adjacent channels share enoughinformation to provide images with similar enough information but independentnoise. We demonstrate several representative case studies, namely spectroscopic(k-edge) X-ray tomography, in vivo X-ray cine-radiography, andenergy-dispersive (Bragg edge) neutron tomography. In all cases, the N2N methodshows dramatic improvement and outperforms conventional denoising methods. Forsuch imaging techniques, the method can therefore significantly improve imagequality, or maintain image quality with further reduced exposure time perimage.

Shot Noise Reduction in Radiographic and Tomographic Multi-Channel Imaging with Self-Supervised Deep Learning