Improving Generalization of Deep Learning–Based Ring Artefact Removal in X-ray Microtomography Imaging of Geomaterials

High-resolution X-ray microtomography (XMT) imaging of rock deformation experiments at micrometer scale provide valuable insights into the coupled evolution of pores, cracks, and fluid pathways (Noiriel & Renard, 2022). A critical step in XMT data processing is the removal of ring artefacts, which is attributed to malfunctioning detector components within the acquisition system (Vo et al., 2018). These artefacts appear as stripes in the raw acquired sinogram domain and concentric circles in reconstructed images. Ring artefacts can adversely affect downstream analyses such as pore and fracture segmentation, and digital volume correlation (DVC) (Mahdaviara et al., 2025). Advances in GPU computing and ML-based image processing have led the synchrotron community to explore deep learning architectures including ResUNET (Fu et al., 2023), and attention-based variants (Zhang et al., 2022) to suppress ring artefacts. Most ML-based denoisers rely on single-domain, pixel-based loss functions, such as L₁, L₂, or structural similarity index measure (SSIM), applied either in the sinogram or reconstructed image domain.

This study investigates a dual-domain loss function that combines loss terms in the sinogram domain with those in the corresponding Fast Fourier Transform magnitude (FFT amplitude) domain, aiming to improve generalization of trained U-Net variants. Existing artefact-free XMT images of basalt were used to simulate stripe artefacts in its raw sinogram domain. Stripe artefact generation was controlled using three parameters: pixel thickness, amplitude, and number of stripes per sinogram. A total of 5,000 paired noisy and clean sinograms were generated and split into training, validation, and test datasets. Three UNET-based architectures were evaluated: a baseline U-Net (baseUNET), a residual U-Net (ResUNET), and a residual U-Net with attention gates (AG-ResUNET). Models were trained for 100 epochs using the Adam optimiser and their performances were assessed using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and qualitative inspection of sinograms, reconstructed images, and FFT amplitude spectra. The study compares single- and dual-domain loss functions in terms of ring artefact suppression and generalization beyond the training data distribution, discusses limitations related to the dynamic range of the training data and its implications for denoising experimental XMT datasets.

References

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