arXiv:2602. 21987v3 Announce Type: replace-cross Abstract: Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but their quality is often degraded by noise from low-dose acquisition, patient motion, or scanner limitations, affecting both clinical interpretation and downstream analysis.
By Jitindra Fartiyal, Pedro Freire, Sergei K. Turitsyn, Sergei G. Solovski
arXiv:2607. 11941v1 Announce Type: cross Abstract: Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and structured artifacts that degrade image quality.
By Md Imam Ahasan, Guangchao Yang, A F M Abdun Noor, Kah Ong Michael Goh, S. M. Hasan Mahmud, Md Mahfuzur Rahman
arXiv:2606. 04299v1 Announce Type: cross Abstract: We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image.
By Haojun Qiu, Kiriakos N. Kutulakos, David B. Lindell
arXiv:2507. 06764v5 Announce Type: replace-cross Abstract: In this work, we propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to rapidly and efficiently train deep imaging networks without ground-truth data.
By Guixian Xu, Jinglai Li, Junqi Tang
arXiv:2606. 15611v1 Announce Type: cross Abstract: Organ segmentation from PET/CT is critical for quantitative analysis and radiotherapy planning in oncology.
By Fuyou Mao, Beining Wu, Yanfeng Jiang, Bohan Xu, Lixin Lin, Naye Ji, Hao Zhang, Yan Tang
Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt large pretrained text-to-image (T2I) latent diffusion models for their strong capacity and generative priors.