AQFC2015

Harvesting sparsity in imaging sciences

 


Department of Systems Engineering and Engineering Management
The Chinese University of Hong Kong

Title: Harvesting sparsity in imaging sciences
 
Speaker: Dr. Yifei Lou,
Department of Mathematics, University of California Irvine
 
Date: May 30, 2014 (Friday)
 
Time: 4:30 PM to 5:30 PM
 
Venue: Room 513
       William M.W. Mong Engineering Building (ERB)
       (Engineering Building Complex Phase 2)
       The Chinese University of Hong Kong.

Short Biography of the speaker:
Yifei Lou received her Ph.D. in Applied Math from the University of California Los Angeles (UCLA) in 2010 when she was co-supervised by Prof. Andrea Bertozzi in Math Dept. and Prof. Stefano Soatto in Computer Science Dept. After graduation, she joined the School of Electrical and Computer Engineering, Georgia Institute of Technology as a postdoctoral fellow, supervised by Prof. Allen R. Tannenbaum. A year later, she has a joint position as a visiting lecturer at Department of Mathematics, University of California San Diego and a postdoc at UCLA. Now she is a postdoc working with Prof. Jack Xin and Prof. Hongkai Zhao at Department of Mathematics, University of California Irvine. Her research interests are: Image restoration and medical imaging; Sparse coding and its applications; Imaging through turbulence; Optimization algorithms.
 
Abstract:
Sparse representation has attracted tremendous attention over the past decade. Particularly in imaging sciences, it has been widely employed as prior information to facilitate a wide spectrum of image reconstruction problems, where the underlying image can be sparsely represented under a set of basis functions. One important issue behind these problems is the choice of such basis functions. An appropriately chosen basis set can have better sparse representation of an image, thus yielding better results. In this talk, we target the basis selection in the problem of image deblurring. Our proposed algorithm outperforms the state-of-the-art, especially in deblurring text images. During the course, we have observed a limitation of this approach in terms of coherence. It is the same limitation that many conventional methods have when solving sparsity-related problems. We then propose a novel technique that can promote sparsity even in highly coherent scenarios. Satisfactory experimental results have been observed; and theoretical aspects behind this approach have also been discovered.
 
                      Everyone is invited to attend the talk.
 
The talk will be hosted by:
       Prof. Shiqian Ma,
       Department of Systems Engineering and Engineering Management,
       The Chinese University of Hong Kong,
       E-mail: sqma@se.cuhk.edu.hk
       Telephone Number: (852) 3943-8240
 
For general enquiries, please contact the student coordinator:
       Andy Chung,
       Department of Systems Engineering and Engineering Management,
       The Chinese University of Hong Kong,
       E-mail: oychung@se.cuhk.edu.hk
 
SEEM-5202 Website: http://seminar.se.cuhk.edu.hk
Date: 
Friday, May 30, 2014 - 08:30 to 09:30