arXiv:2609.25429v1 Announce Type: new
Abstract: In this paper, we introduce the Directional Total Variation-Regularized Implicit Neural Representation (DTV-INR), an advanced variational paradigm that...
By Mahmoud Saeedi Kelishami
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.
By Laura Hellwege, Johann Christopher Engster, Moritz Schaar, Thorsten M. Buzug, Maik Stille
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...
By Shirin Shoushtari, Edward P. Chandler, Xiao Shi, Ulugbek S. Kamilov
This paper introduces a fully differentiable jitter correction technique for X‑ray phase‑contrast micro‑CT that uses a deep learning‑based image quality metric to estimate and compensate per‑projection rigid jitter directly from the acquired data, eliminating the need for a motion‑free reference scan. The method adapts a gradient‑based auto‑focus strategy to parallel‑beam geometry, benchmarks several objective functions, and validates the sensitivity of the visual information fidelity (VIF) metric to jitter artifacts. A compact 3D CNN predicts VIF scores from corrupted volumes, while a spatially selective total variation penalty suppresses spurious high‑frequency structures during optimization; experiments on biological specimens from multiple synchrotron beamlines confirm that the pipeline reliably restores fine structural detail across morphologically distinct samples.
By Junan Chen, Yiting Jia, Joscha Maier, Dominik John, Sami Wirtensohn, Imke Greving, Silja Flenner, Matthias Wieczorek, Julia Herzen
The paper introduces CoSMo-RecNet, a modular framework for multi-contrast MRI reconstruction that operates effectively in low-data regimes. It leverages a reusable content/style prior learned from large, unpaired multi-contrast image datasets, allowing a lightweight unrolled network to refine reconstructions using only a few task‑specific training samples. Experiments on low‑field 0.3 T and ultra‑low‑field 47 mT datasets demonstrate that CoSMo-RecNet outperforms parameter‑matched MoDL, classical reconstruction, transfer learning, and zero‑shot methods, achieving higher quality with as few as five training subjects.
By Chinmay Rao, Efe Il{\i}cak, Matthias J. P. van Osch, Mariya Doneva, Laurens Beljaards, Navid Jabarimani, Nicola Pezzotti, Marius Staring
arXiv:2606. 07675v1 Announce Type: cross Abstract: Smartphone telephoto cameras are approaching a "telephoto physics wall": as pixel pitches shrink toward sub-0.
By Jingxi Li, Neerja Aggarwal, Laurent Gudemann, Shivansh Rao, Vishal Vinod, Tom E. Bishop, Ziv Attar