5 papers
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…
Blur2seq: Blind Deblurring and Camera Trajectory Estimation from a Single Camera Motion-blurred Image
Guillermo Carbajal, Andrés Almansa, Pablo Musé
Motion blur caused by camera shake, particularly under large or rotational movements, remains a major challenge in image restoration. We propose a deep learning framework that join…
LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization
Alessio Spagnoletti, Jean Prost, Andrés Almansa +2
Text-to-image latent diffusion models (LDMs) have recently emerged as powerful generative models with great potential for solving inverse problems in imaging. However, leveraging s…
Infusion: internal diffusion for inpainting of dynamic textures and complex motion
Nicolas Cherel, Andrés Almansa, Yann Gousseau +1
Video inpainting is the task of filling a region in a video in a visually convincing manner. It is very challenging due to the high dimensionality of the data and the temporal cons…
Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models
Marien Renaud, Jiaming Liu, Valentin de Bortoli +2
Posterior sampling has been shown to be a powerful Bayesian approach for solving imaging inverse problems. The recent plug-and-play unadjusted Langevin algorithm (PnP-ULA) has emer…