arXiv:2608. 05839v1 Announce Type: cross Abstract: Deep neural networks have shown great empirical success in the solution of a wide variety of ill-posed inverse problems in imaging.
arXiv:2508. 05321v4 Announce Type: replace-cross Abstract: Assume you encounter an inverse problem that shall be solved for a large number of data, but no ground-truth data is available.
arXiv:2606. 00078v1 Announce Type: cross Abstract: Numerous modern applications in signal processing and medical imaging necessitate acquiring high-dimensional signals under tight resource constraints.
arXiv:2512. 08444v2 Announce Type: replace-cross Abstract: Learned image reconstruction has become a pillar in computational imaging and inverse problems.
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.
arXiv:2607. 07189v1 Announce Type: new Abstract: Vision-language models (VLMs) and agentic AI have shown strong performance on semantic visual tasks, but it remains unclear whether they can handle the physics and inverse problems that underlie computational imaging.
X-ray computed tomography reconstruction is an ill-posed inverse problem, particularly in low-dose and sparse-angle settings where measurements are noisy and incomplete. While learned reconstruction methods such as the Learned Primal-Dual algorithm achieve strong performance, they typically rely on supervised training with access to ground-truth data, which is often unavailable in practice.
arXiv:2610.04624v2 Announce Type: replace Abstract: Recovering Partial Differential Equation (PDE) coefficient fields from extremely sparse observations is a severely ill-posed inverse problem for wh...
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:2603. 04438v3 Announce Type: replace-cross Abstract: Fully unsupervised deep generative modeling (FU-DGM) offers significant potential for compressively sampled magnetic resonance imaging (CS-MRI) reconstruction.
arXiv:2606. 26991v1 Announce Type: cross Abstract: X-ray computed tomography reconstruction is an ill-posed inverse problem, particularly in low-dose and sparse-angle settings where measurements are noisy and incomplete.
The paper introduces Prior-Consistent DAPS (PC‑DAPS), a variant of Decoupled Annealing Posterior Sampling designed to handle ambiguous nonlinear inverse problems where multiple measurement‑consistent solutions exist. PC‑DAPS maintains several candidate solutions in a single sampling run and prunes or replaces weaker candidates based on prior consistency as annealing noise decreases, without requiring ground‑truth access. This approach reduces prior‑inconsistent samples and improves sample quality for nonlinear inverse problems while preserving quality and diversity for linear ones.