Recently, there have been important developments on sparse representation of signals and images. It also attracted a lot of attention among researchers in the area of signal and image processing, and is applied to many important applications, including data acquisition, image compression, image restoration, multispectral image representation, etc. There are three major problems in researches related to sparse representation of signals and images: How to sparsely represent an image, What are characteristics of data after it is sparsely represented, and What we can do with it after a sparse representation is performed on an image. In this seminar, I will introduce some of our recent research works, which address all three major problems of sparse representation. Specifically, it will include major techniques such as Dictionary learning, Total variation, and Compressed sensing in this talk. Some of interesting results of our work will also be presented and explained.
Peng Liu received the M.S. degree in 2004 and the Ph.D. degree in 2009, both from Chinese Academic of Science. Currently, he is an Associate Professor at the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. From May 2012 to May 2013, he was a Visiting Scholar with Department of Electrical and Computer Engineering, George Washington University, and was hosted by Professor Kie B. Eom. His research is focused on Sparse representation of images, Compressive sensing, Image processing and their applications to remote sensing.
Phillips Hall 640
801 22nd St. NW
If there are any questions, please contact Dr. Lan at firstname.lastname@example.org.