3 papers
eess.IV2026
Bayesian model selection and misspecification testing in imaging inverse problems only from noisy and partial measurements
Tom Sprunck, Marcelo Pereyra, Tobias Liaudat
Modern imaging techniques heavily rely on Bayesian statistical models to address difficult image reconstruction and restoration tasks. This paper addresses the objective evaluation…
stat.ML2026
Consistency Regularised Gradient Flows for Inverse Problems
Alessio Spagnoletti, Tim Y. J. Wang, Marcelo Pereyra +1
Vision-Language Latent Diffusion Models (LDMs) (Rombach et al., 2022) provide powerful generative priors for inverse problems. However, existing LDM-based inverse solvers typically…
cs.CV2026
LVTINO: LAtent Video consisTency INverse sOlver for High Definition Video Restoration
Alessio Spagnoletti, Andrés Almansa, Marcelo Pereyra
Computational imaging methods increasingly rely on powerful generative diffusion models to tackle challenging image restoration tasks. In particular, state-of-the-art zero-shot ima…