Title:
Advanced Sparse Representation Models for Image Analysis
Abstract:
Intrinsic structures of high-dimensional visual data often have the properties of low dimensionality, sparsity, or degeneracy. By discovering and properly harnessing these intrinsic structures, groundbreaking results have been achieved in the past decade on diverse applications in the domains of signal/image processing, computer vision, and machine learning. This talk aims to present the very recent breakthroughs in image processing research. These results are achieved by leveraging new powerful, more advanced sparse representation models and by developing efficient large-scale optimization algorithms.
This talk will present the very recent breakthroughs of sparse/low-rank models in image processing. These results are essentially obtained by properly harnessing these rich low-dimensional structures prevailing in natural images, using carefully designed more advanced models with sparsity/low-rank constriants, for various low-level vision tasks including image de-noising, de-blurring, super-resolution etc. We will specially show that how local sparsity/low-rank models and non-local self-similarity of natural images are exploited to achieve these breakthrough results.Bio:
Weisheng Dong received the B. S degree in communication engineering from Huazhong University of science and technology, Wuhan, China in 2004 and the Ph. D. degree in circuit and systems from Xidian University, Xi’an, China in 2010. From Jan. 2009 to Jun. 2010, he was a research assistant / associate with Dep. of Computing, the Hong Kong Polytechnic University, Hong Kong. From Aug. 2012 to Feb. 2013 he was a visiting researcher with
Microsoft Research Asia. In Sep. 2010 he joined the school of electronic engineering, Xidian University as a Lecturer, and has been an associate professor since Jun. 2012. His research interests include inverse problems in image processing, sparse representation, and image compression. He was the recipient of the best paper award at SPIE VCIP 2010.