arXiv Machine Learning June 5, 2026 By Yujia Wu, Zhaoqiang Liu

Tracing the Oracle: Improving Diffusion Timestep Scheduling for 3D CT Reconstruction

Read the original on arXiv Machine Learning →

arXiv:2606. 06236v1 Announce Type: new Abstract: Pretrained diffusion models demonstrate impressive potential in solving highly ill-posed 3D computed tomography (CT) inverse problems, while the inference process suffers from significant computational overhead.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv:2603. 00205v2 Announce Type: replace-cross Abstract: Generative models, particularly Diffusion Models (DM), have shown strong potential for Computed Tomography (CT) reconstruction serving as expressive priors for solving ill-posed inverse problems.

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DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing

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Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing

arXiv:2604. 17838v2 Announce Type: replace Abstract: Generative modeling within constrained sets is essential for scientific and engineering applications involving physical, geometric, or safety requirements (e.

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Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction

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PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise Latent Diffusion Prior

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Efficient Flow Matching for Sparse-View CT Reconstruction

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