CDPIR integrates cross-distribution diffusion priors from a Scalable Interpolant Transformer trained with classifier-free guidance into model-based iterative reconstruction to improve sparse-view CT under out-of-distribution conditions.
Improving diffusion inverse problem solving with decoupled noise an- nealing
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Diffusion priors for sparse-view CT work on synthetic data but face domain shift and forward model mismatch on experimental phantom data, with annealed likelihood weights offering partial mitigation.
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Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT
CDPIR integrates cross-distribution diffusion priors from a Scalable Interpolant Transformer trained with classifier-free guidance into model-based iterative reconstruction to improve sparse-view CT under out-of-distribution conditions.
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Towards reconstructing experimental sparse-view X-ray CT data with diffusion models
Diffusion priors for sparse-view CT work on synthetic data but face domain shift and forward model mismatch on experimental phantom data, with annealed likelihood weights offering partial mitigation.