The paper introduces Consecutive Posterior Fusion Denoising Diffusion Null‑Space Models (CPF‑DDNM), an inference‑time method that fuses successive measurement‑aware estimates to better recover unobservable image structures in severely ill‑posed imaging inverse problems. CPF‑DDNM builds on DDNM’s range/null‑space decomposition, preserving measurement‑determined components while refining prior‑driven null‑space estimates. Experiments on sparse‑view CT, low‑dose CT, and medical image super‑resolution demonstrate consistent improvements over DDNM and competitive performance against other diffusion‑based inverse solvers.
By Elena Morotti, Davide Evangelista, Elena Loli Piccolomini
Score-based diffusion models, a recent framework for posterior sampling in Bayesian inverse problems, are applied to diffuse optical tomography (DOT), a highly ill‑posed boundary value problem for recovering tissue absorption and scattering. The authors introduce a mixed score that combines a learned component with a model‑based component, providing a theoretical justification for its local approximation to the true score in the small diffusion‑time regime. Four difference‑imaging approaches are compared—classical model‑based, approximate diffusion, exact posterior sampling (UCoS), and a regularized UCoS—showing that UCoS yields more accurate reconstructions, especially under limited‑view geometry and real experimental data.
By Fabian Schneider, Meghdoot Mozumder, Konstantin Tamarov, Leila Taghizadeh, Tanja Tarvainen, Tapio Helin, Duc-Lam Duong
arXiv:2609.24839v1 Announce Type: new
Abstract: Feed-forward 3D reconstruction models enable efficient geometry estimation from sparse images, but their pretrained nature can make them vulnerable to...
By Daisy Li, Kyle Gao, Quanyun Wu, Hanna Chomko, John S. Zelek, Jonathan Li
arXiv:2602. 23214v2 Announce Type: replace-cross Abstract: Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained generative models as modular priors.
By Chenhe Du, Xuanyu Tian, Qing Wu, Muyu Liu, Jingyi Yu, Hongjiang Wei, Yuyao Zhang
arXiv:2606. 02331v1 Announce Type: cross Abstract: Diffusion-based inverse problem solvers can produce realistic reconstructions, but realism alone does not ensure that the recovered details are supported by the measurement.
By Pengfei Jin, Yiqi Tian, Kailong Fan, Bingjie Qi, Quanzheng Li
The paper introduces RetinaDiff, a physics‑informed conditional diffusion framework for retinal temporal laser speckle contrast imaging (tLSCI). It first stabilizes speckle sequences via phase‑correlation registration, then uses a motion‑corrected contrast prior to guide a diffusion model that reconstructs high‑quality tLSCI maps from only a few frames. Experiments on an in‑house retinal LSCI system show significant improvements in SSIM, PSNR, and FID compared to conventional five‑frame reconstruction, even in challenging cases.
By Qian Chen, Yuehao Chen, Qiang Wang, Yutao Feng, Lei Zhu, Yanye Lu