Structured Low-Rank Matrix Recovery via Optimization Methods

Event Sponsor: 
Mathematics and Computer Science Seminar
Start Date: 
Feb 23 2018 - 10:30am
Building/Room: 
Building 240/Room 4301
Location: 
Argonne National Laboratory
Speaker(s): 
Dehui Yang
Speaker(s) Title: 
Colorado School of Mines
Host: 
Prasanna Balaprakash

Motivated by applications from single-molecule microscopy in biology to computational imaging in astronomy, in this talk, I will talk about the problem of non-stationary blind super-resolution, in which the point spread functions associated with point sources need to be calibrated. To do this, I propose a flexible atomic norm minimization framework to solve this daunting inverse problem. Along the way, I also derive a sample complexity bound that is optimal for this problem. This optimization framework also inspires new sensing strategies for modal analysis in structural health monitoring. At the end of the talk, I will also discuss my summer internship project at Technicolor Research. Motivated by the business at Technicolor, I will explore the possibility of using deep neural networks for standard dynamic range (SDR) to high dynamic range (HDR) wide color conversion. Data collection, deep neural network models training and testing, as well as experimental results will be presented.

Miscellaneous Information: 

This seminar will be streamed, see details at https://anlpress.cels.anl.gov/mcs-streaming-seminars.

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