SPS Webinar: A Walk Through Image Deblurring: From Model-Based to Generative Restoration

Date: 29 March 2023
Time: 12:30 PM ET (New York Time)
Presenter(s): Dr. Mauricio Delbracio
Download article: Freely available to download on the day of the webinar for 48 hours


Mauricio DelbracioMauricio Delbracio received his B.Sc. in Electrical Engineering from Universidad de la República in 2006 and his M.Sc. and Ph.D. in Applied Mathematics from École Normale Supérieure de Cachan in 2009 and 2013 respectively.

He is a currently a Senior Research Scientist at Google Research. Prior to joining Google in 2019, he was an Assistant Professor at the Electrical Engineering Department of Universidad de la República in Uruguay. From 2013 to 2016 he was a postdoctoral researcher with the ECE Department at Duke University.  His research focuses on image and signal processing, computational imaging, inverse problems, and machine learning.

Dr. Delbracio was a key contributor to the launch of Unblur, an image deblurring feature introduced with the Google Pixel 7/Pro. In 2016, he was awarded the Early Career Prize from the Society for Industrial and Applied Mathematics' Imaging Science Activity Group for his significant contributions to image processing.


Image deblurring has seen tremendous progress in recent years mostly coming hand-in-hand with the success of deep neural networks. Greater computational power, reliable and accessible training frameworks and large amounts of data have enabled deep image processing models that exceed or are on par with those conceived through careful and artisan modeling. During this talk I will present our recent work on image deblurring with a focus on two distinct scenarios.  First, I will introduce Polyblur, a highly efficient blind restoration method for removing mild blur in natural images. Polyblur estimates slight image blur and compensates for it by combining multiple applications of the estimated blur allowing processing of a 12MP photo on a modern mobile phone in a fraction of a second. In the second part of the talk, I will discuss how to train deep image enhancement models for improved realism in restored images. I will present an alternative approach using a conditional diffusion model to stochastically refine the output of a deterministic predictor capable of producing realistic results. To conclude this talk, I will showcase the newly introduced Unblur feature in the Google Pixel 7/Pro.