Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning - Zharov, Yaroslav; Ametova, Evelina; Spiecker, Rebecca; Baumbach, Tilo; Burca, Genoveva; Heuveline, Vincent (Optics express, 2023)
Shot noise is a critical issue in radiographic and tomographic imaging, especially when additional constraints lead to a significant reduction of the signal-to-noise ratio. This paper presents a method for improving the quality of noisy multi-channel imaging datasets, such as data from time or energy-resolved imaging, by exploiting structural similarities between channels. To achieve that, we broaden the application domain of the Noise2Noise self-supervised denoising approach. The method draws pairs of samples from a data distribution with identical signals but uncorrelated noise. It is applicable to multi-channel datasets if adjacent channels provide images with similar enough information but independent noise. We demonstrate the applicability and performance of the method via three case studies, namely spectroscopic X-ray tomography, energy-dispersive neutron tomography, and in vivo X-ray cine-radiography.
Online veröffentlicht am 24. Juli 2023
Gesehen am 07.12.2023