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Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network
[article]
2021
arXiv
pre-print
Low-dose CT has been a key diagnostic imaging modality to reduce the potential risk of radiation overdose to patient health. Despite recent advances, CNN-based approaches typically apply filters in a spatially invariant way and adopt similar pixel-level losses, which treat all regions of the CT image equally and can be inefficient when fine-grained structures coexist with non-uniformly distributed noises. To address this issue, we propose a Structure-preserving Kernel Prediction Network
arXiv:2105.14758v3
fatcat:nlciihoyszealfxmhnmlan3qim