54 citations · 324 across the 32 of their papers we have counts for
6 papers · 1 filter
Stochastic Deep Restoration Priors for Imaging Inverse Problems
Yuyang Hu, Albert Peng, Weijie Gan +3
Deep neural networks trained as image denoisers are widely used as priors for solving imaging inverse problems. While Gaussian denoising is thought sufficient for learning image pr…
A Restoration Network as an Implicit Prior
Yuyang Hu, Mauricio Delbracio, Peyman Milanfar +1
Image denoisers have been shown to be powerful priors for solving inverse problems in imaging. In this work, we introduce a generalization of these methods that allows any image re…
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
Mauricio Delbracio, Peyman Milanfar
Inversion by Direct Iteration (InDI) is a new formulation for supervised image restoration that avoids the so-called "regression to the mean" effect and produces more realistic and…
Interpretable Unsupervised Diversity Denoising and Artefact Removal
Mangal Prakash, Mauricio Delbracio, Peyman Milanfar +1
Image denoising and artefact removal are complex inverse problems admitting multiple valid solutions. Unsupervised diversity restoration, that is, obtaining a diverse set of possib…
Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data
Abdullah Abuolaim, Mauricio Delbracio, Damien Kelly +2
Recent work has shown impressive results on data-driven defocus deblurring using the two-image views available on modern dual-pixel (DP) sensors. One significant challenge in this…
A Practical Guide to Multi-image Alignment
Cecilia Aguerrebere, Mauricio Delbracio, Alberto Bartesaghi +1
Multi-image alignment, bringing a group of images into common register, is an ubiquitous problem and the first step of many applications in a wide variety of domains. As a result,…