FrequencyCT is a frequency-domain zero-shot self-supervised method that creates pseudo-samples via low-frequency anchoring, phase-preserving high-frequency perturbations, and signal-correlated truncation for low-dose CT denoising.
TorchRadon: Fast Differentiable Routines for Computed Tomography
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Neighbor2Inverse adapts the Neighbor2Neighbor principle to train a denoising network directly in the image domain for low-dose PBI-CT by using independently noised subsampled projections.
SimAQ generates synthetic soft X-ray tomography data with realistic artifacts to train segmentation models that transfer effectively to real yeast tomograms via few-shot and zero-shot learning.
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FrequencyCT: Frequency Domain Self-supervised Low-dose CT Denoising
FrequencyCT is a frequency-domain zero-shot self-supervised method that creates pseudo-samples via low-frequency anchoring, phase-preserving high-frequency perturbations, and signal-correlated truncation for low-dose CT denoising.
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Neighbor2Inverse: Self-Supervised Denoising for Low-Dose Region-of-Interest Phase Contrast CT
Neighbor2Inverse adapts the Neighbor2Neighbor principle to train a denoising network directly in the image domain for low-dose PBI-CT by using independently noised subsampled projections.