The paper presents a robust covariance estimation method for data that is both entry-wise contaminated and partially observed. It extends the Student‑t* model by using a stochastic approximation EM algorithm that operates on a reduced latent space, avoiding explicit sampling of latent Gaussian variables while maintaining a likelihood‑based framework. Experiments on synthetic and real sensor data demonstrate that this approach achieves lower Fisher‑Rao error than Gaussian, Student‑t, imputation‑based, and detect‑then‑estimate estimators, especially at moderate to high outlier rates under diffuse contamination.
By Matys Savidan, Mohammed Nabil El Korso
arXiv:2611.06784v1 Announce Type: cross
Abstract: We study the fundamental limits of jointly estimating the signal amplitude and the atmospheric phase distortion in complex-valued multi-look coherent...
By Songbin Liu, Hanyu Yang, Junren Chen, Arian Maleki
arXiv:2607. 03761v1 Announce Type: new Abstract: Computed Tomography (CT) is indispensable in clinical diagnostics, yet minimizing radiation dose without compromising image quality remains a critical challenge.
By Shunta Nonaga, Koji Tabata, Junya Honda, Hiroyuki Kudo, Wataru Yashiro, Tamiki Komatsuzaki
Generative Translation Priors (GTP) is a Bayesian framework that repurposes diffusion-based image-to-image translation models as cross-modality priors for ill‑posed imaging inverse problems. It incorporates target‑domain measurements via likelihood guidance, steering the translation toward the desired posterior distribution. The authors analyze the resulting posterior dynamics, identify an intrinsic bias from likelihood guidance, and propose a ground‑truth‑free metric to estimate this bias, leading to two discretized GTP algorithms that demonstrate high‑fidelity reconstruction in CT and PET imaging using complementary side information.
By Evan Bell, Jiaming Liu, Yifan Chen, Yu Sun
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...
By Qi Yu, Hanlin Wu, Xiaohui Sun
arXiv:2606. 03074v1 Announce Type: new Abstract: Diffusion models achieve high-fidelity radio map construction through iterative denoising, yet their sampling cost limits practicality in dynamic wireless systems where radio maps must be refreshed repeatedly.
By Zixuan Guo, Xiucheng Wang, Nan Cheng
The paper introduces RED-KLwSGS, a new posterior sampling method for Bayesian imaging inverse problems that combines exact Gaussian updates for the data variable with underdamped (kinetic) Langevin diffusion for the auxiliary variable, driven by a one‑shot denoising score. It achieves the same per‑iteration cost as existing Langevin‑within‑SGS samplers while providing non‑asymptotic Wasserstein‑2 convergence guarantees for strongly log‑concave priors. A further variant, Joint-RED-KLwSGS, applies kinetic Langevin diffusion to both variables, and experiments using denoising diffusion probabilistic models on FFHQ and ImageNet demonstrate faster convergence and high‑quality image reconstruction.
By Dai Hai Nguyen, Duc Dung Nguyen
arXiv:2610.31920v1 Announce Type: cross
Abstract: In traditional Gaussian process (GP) regression, the inputs are typically assumed to be known, and uncertainty enters only through the measurements....
By Muzaffar Qureshi, Tochukwu E. Ogri, Trivikram Satharasi, Kyle Volle, Rushikesh Kamalapurkar
arXiv:2607. 20909v1 Announce Type: cross Abstract: Radio map (RM) estimation aims to reconstruct the spatial distribution of wireless signal characteristics, such as received signal strength (RSS), from sparse measurements, a task that is critical for spectrum management, interference mitigation, and localization in modern wireless networks.
By Liu Yang, Qiang Li, Zhuo Cao, Weijie Xiong, Guomin Sun, Jingran Lin
The paper introduces a physics‑guided, data‑driven framework for reconstructing dense ultrasound RF data from sparse acquisitions. It trains an end‑to‑end interpolation network with a hybrid RF‑ and beamforming‑domain loss, stabilized by exponential moving average, and employs random‑skip masking to generalize across varying sparsity patterns and channel configurations. On a held‑out test set, the method achieves a mean SSIM of about 0.95 across decimation factors from ×2 to ×13, consistently improving RF reconstruction and post‑beamforming image quality.
By Luoyuan Zhang, Yiyang You, Ananya Tandri, Yinan Feng, Hyunwoo Song, Jeeun Kang, Youzuo Lin
arXiv:2607. 23018v1 Announce Type: cross Abstract: Nonstationary Gaussian process (GP) models are powerful tools for capturing input-dependent variability by adapting to observed data.
By Jeremy Ovadia
In traditional Gaussian process (GP) regression, the inputs are typically assumed to be known, and uncertainty enters only through the measurements. When scalar fields are mapped using GP regression w...