mauricio delbracio

I'm a research scientist at Google Research, where I work on on computational imaging and machine learning. My current work is at the intersection & interaction of machine learning, signal and image processing and computer vision. I'm interested in finding new ways of applying machine learning to signal processing and to general inverse problems.

At Google Research, I have contributed to the development of several core technologies including Sharpen & Denoise and the Unblur feature launched with Pixel 7/pro [review].

Previously, I was an Assistant Professor in Electrical Engineering at Universidad de la República, Montevideo, Uruguay. Before that, I was a postdoctoral researcher in Guillermo Sapiro’s group, at the ECE Department in Duke University. And before that, I was a Ph.D. student at CMLA, ENS-Cachan, France under the supervision of Jean-Michel Morel, Pablo Musé and Andrés Almansa.

In this page you will find some pointers to my work. Please feel free to contact me if you want more information, have any comments, suggestions.

An old talk that I gave summarizing my work for the 2016 SIAM Imaging Early Career Prize can be found here.

Full list of publications in Google Scholar.

mdelbra [at] gmail.com

Papers

A Restoration Network as an Implicit Prior

Y, Hu, M. Delbracio, P. Milanfar, U.S. Kamilov

Preprint, 2023

[arXiv]

Conditional Diffusion Distillation

K. Mei, M. Delbracio, H. Talebi, Z. Tu, V.M. Patel, P. Milanfar

Preprint, 2023

[arXiv]

Prompt-tuning latent diffusion models for inverse problems

H. Chung, J.C. Ye, P. Milanfar, M. Delbracio

Preprint, 2023

[arXiv]

Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration

M. Delbracio, P. Milanfar

TMLR, 2023 (Featured Certification)

[openreview] [web] [arXiv]

Multiscale Structure Guided Diffusion for Image Deblurring

M. Ren, M. Delbracio, H. Talebi, G. Gerig, P. Milanfar

ICCV, 2023

[pdf] [arXiv]

Soft Diffusion: Score matching for general corruptions

G. Daras, M. Delbracio, H. Talebi, A. G. Dimakis, P. Milanfar

TMLR, 2023

[openreview] [arXiv]

Deblurring via stochastic refinement

J, Whang, M. Delbracio, H. Talebi, C. Saharia, A. G. Dimakis, P. Milanfar

CPVR, 2022

[pdf] [arXiv]

Interpretable Unsupervised Diversity Denoising and Artefact Removal

M. Prakash, M. Delbracio, P. Milanfar, F. Jug

ICLR, 2022

[openreview] [arXiv] [github]

Mobile Computational Photography: A Tour

M. Delbracio, D. Kelly, M. S. Brown, P. Milanfar

Annual Review of Vision Science, 2021

[doi] [arXiv]

Projected Distribution Loss for Image Enhancement

M. Delbracio, H. Talebi, P. Milanfar

ICCP, 2021

[doi] [arXiv]

Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data

A. Abuolaim, M. Delbracio, D. Kelly, M.S. Brown, P. Milanfar

ICCV, 2021

[pdf] [arXiv]

Polyblur: Removing mild blur by polynomial reblurring

M. Delbracio, I. Garcia-Dorado, S. Choi, D. Kelly, P. Milanfar

IEEE TCI, 2021

[pdf] [arXiv]

Single Image Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition

G. Carbajal, P. Vitoria, M. Delbracio, P. Musé, J. Lezama

Preprint, 2021

[arXiv]

Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method

J.M. Di Martino, F. Suzacq, M. Delbracio, Q. Qiu, G. Sapiro

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020

[doi] [arXiv]

Solving Inverse Problems by Joint Posterior Maximization with a VAE Prior

M. Gonzalez, A. Almansa, M. Delbracio, P. Musé, P. Tan

Preprint, 2019

[arXiv]

Efficient blind deblurring under high noise levels

J. Anger, M. Delbracio, G. Facciolo

International Symposium on Image and Signal Processing and Analysis (ISPA), 2019

[arXiv]

Image Anomalies: a Review and Synthesis of Detection Methods

T. Ehret, A. Davy, J.-M. Morel, M. Delbracio

Journal of Mathematical Imaging and Vision (JMIV), 2019 (to appear)

[arXiv]

Image Anomalies: a Review and Synthesis of Detection Methods

T. Ehret, A. Davy, J.-M. Morel, M. Delbracio

Journal of Mathematical Imaging and Vision (JMIV), 2019 (to appear)

[arXiv]

Modeling Realistic Degradations in Non-blind Deconvolution

J. Anger, M. Delbracio, G. Facciolo

IEEE International Conference on Image Processing (ICIP), 2018

[arXiv]

Reducing Anomaly Detection in Images to Detection in Noise

A. Davy, T. Ehret, J.-M., M. Delbracio

IEEE International Conference on Image Processing (ICIP), 2018

A Practical Guide to Multi-image Alignment

C. Aguerrebere, M. Delbracio, A. Bartesaghi, G. Sapiro

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018

[arXiv]

Deep Video Deblurring

S. Su, M. Delbracio, J. Wang, G. Sapiro, W. Heidrich, O. Wang

IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2017

[web] [code] [arXiv]

Fundamental Limits in Multi-image Alignment

C. Aguerrebere, M. Delbracio, A. Bartesaghi, G. Sapiro

IEEE Transactions on Signal Processing (TSP), 2016

[doi] [arXiv] [bib]

An analysis of the factors affecting keypoint stability in scale-space

I. Rey-Otero, J.-M. Morel, M. Delbracio

Journal of Mathematical Imaging and Vision (JMIV), 2016

[doi] [arXiv] [bib]

Hand-held Video Deblurring via Efficient Fourier Aggregation

M. Delbracio and G. Sapiro

IEEE Transactions on Computational Imaging (TCI), 2015

[web] [doi] [arXiv] [bib]

Removing Camera Shake via Weighted Fourier Burst Accumulation

M. Delbracio and G. Sapiro

IEEE Transactions on Image Processing (TIP), 2015

[web] [doi] [arXiv] [bib]

Is Repeatability an Unbiased Criterion for Ranking Feature Detectors?

I. Rey-Otero, M. Delbracio

SIAM Journal on Imaging Sciences (SIIMS), 2015

[doi] [arXiv] [bib]

Burst Deblurring: Removing Camera Shake Through Fourier Burst Accumulation

M. Delbracio and G. Sapiro

IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2015

[web] [doi] [preprint] [bib]

An analysis of scale-space sampling in SIFT

I. Rey-Otero, J.-M. Morel and M. Delbracio

IEEE International Conference on Image Processing (ICIP), 2014

[doi] [preprint] [code] [bib]

Boosting Monte Carlo Rendering by Ray Histogram Fusion

M. Delbracio, P. Musé, A. Buades, J. Chauvier, N. Phelps and J.-M. Morel

Transactions on Graphics (TOG), January 2014.

[web] [doi] [preprint] [demo] [code] [bib]

Subpixel Point Spread Function Estimation from Two Photographs at Different Distances

M. Delbracio, A. Almansa, J.-M. Morel and P. Muse

SIAM Journal on Imaging Sciences (SIIMS), November 2012.

[doi] [preprint] [demo] [code] [bib]

The Non-parametric Sub-pixel Local Point Spread Function Estimation Is a Well Posed Problem

M. Delbracio, P. Muse, A. Almansa and J.-M. Morel

International Journal of Computer Vision (IJCV'2012)

[doi] [preprint] [demo] [code] [bib]

Aguara: An Improved Face Recognition Algorithm through Gabor Filter Adaptation

C. Aguerrebere, G. Capdehourat, M. Delbracio, M. Mateur, A. Fernandez and F. Lecumberry.

IEEE Workshop on Automatic Identification Advanced Technologies (AutoID 2007)

[doi] [preprint]

IPOL - Image Processing Online :: reproducible research

How to Reduce Anomaly Detection in Images to Anomaly Detection in Noise

T. Ehret, A. Davy, M. Delbracio, J.-M. Morel

IPOL, 2019.

[doi] [web] [demo] [code]

Blind Image Deblurring using the l0 Gradient Prior

J. Anger, G. Facciolo, M. Delbracio

IPOL, March 2019.

[doi] [web] [demo] [code]

Estimating an Image's Blur Kernel Using Natural Image Statistics, and Deblurring it: An Analysis of the Goldstein-Fattal Method

J. Anger, G. Facciolo, M. Delbracio

IPOL, September 2018.

[doi] [web] [demo] [code]

Computing an Exact Gaussian Scale-Space

I. Rey Otero, M. Delbracio

IPOL, February 2016.