arXiv:2609.12953v1 Announce Type: new Abstract: Flow matching approaches to imaging inverse problems commonly incorporate measurements in two ways. Conditioning-based approaches supply measurement-de...
arXiv:2607. 19333v1 Announce Type: cross Abstract: Diffusion-based methods have achieved remarkable empirical success in solving inverse problems.
arXiv:2606. 17048v1 Announce Type: new Abstract: Diffusion and flow-based models learn powerful data priors by training a denoiser to reverse Gaussian corruption.
arXiv:2610. 09281v1 Announce Type: new Abstract: In Bayesian inverse problems, posterior sampling requires generating samples that are consistent with given observations while capturing the range of plausible solutions.
The paper introduces Retrieval-Augmented Initialization with Dual-Guided Inversion (RA‑DGI), a method that improves the initial noise used by flow‑based inverse problem solvers by leveraging retrieved proxy clean images and dual‑guided inversion. Experiments across multiple inverse problems show that this improved initialization significantly enhances reconstruction fidelity and perceptual performance compared to standard random noise and inference‑time scaling strategies, while adding less computational overhead.
arXiv:2609.14596v1 Announce Type: new Abstract: Training-free diffusion inverse solvers typically choose between local measurement guidance and costly clean-space posterior updates. Independent poste...
arXiv:2606. 19802v1 Announce Type: new Abstract: Image restoration faces a fundamental tradeoff: methods that minimize error produce blurry reconstructions, while those that maximize perceptual quality yield sharp but less faithful images.
arXiv:2609.37529v1 Announce Type: cross Abstract: Pretrained denoisers provide a powerful way to incorporate image priors into restoration algorithms. Plug-and-Play and RED approaches exploit fixed-n...
arXiv:2610.03503v1 Announce Type: new Abstract: Training-free posterior sampling methods, also known as Plug-and-Play methods, leverage pretrained unconditional diffusion or flow-matching models to s...
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.
The paper introduces MS-Flow, a method that represents a flow-based generative model’s trajectory as a sequence of intermediate latent states instead of a single initial code. By enforcing local flow dynamics and coupling trajectory segments with matching penalties, the approach alternates between updating latent states and ensuring consistency with observed data. This strategy reduces memory usage and improves reconstruction quality on tasks such as image inpainting, super‑resolution, and computed tomography.
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.