5 papers
Iterating Consistency Models: Stability, Error Bounds and Noise Schedules
Alessio Spagnoletti, Abdul-Lateef Haji-Ali, Andrés Almansa +3
Consistency models (CMs) have become a leading approach for generating high-quality samples in few steps. However, adding steps can improve or degrade sample quality in ways that a…
BAM! Bayesian Anything Model: a foundation model for generative computational imaging
Alessio Spagnoletti, Charlesquin Kemajou Mbakam, Jonathan Spence +2
Generative models are transforming Bayesian computational imaging, yet the field still lacks physics-aware foundation models. Current practice falls into two camps. Large foundatio…
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…
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…
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…