Speaker：By Prof.Zixiang Xiong, Texas A &M University, USA

Time： December 17，10：00AM

Venue：Haiyun Admin Building C505

Abstract:

In the past decade, much progress has been made in image denoising due to the use of low-rank representation and sparse coding. In the meanwhile, state-of-the-art algorithms also rely on an iteration step to boost the denoising performance. However, the boosting step is fixed or non-adaptive. In this work, we perform rank-1 based fixed-point analysis, then, guided by our analysis, we develop the first adaptive boosting (AB) algorithm, whose convergence is guaranteed. Preliminary results on the same image dataset show that AB uniformly outperforms existing denoising algorithms on every image and at each noise level, with more gains at higher noise levels.

Biography:

Zixiang Xiong received his Ph.D. degree in electrical engineering from the University of Illinois at Urbana-Champaign in 1996. He is a professor in the ECE department of Texas A&M University and currently visiting  Monash University, Australia. His main research interest lies in image/video processing, networked multimedia, and multi-user information theory. Dr. Xiong received a National Science Foundation Career Award in 1999, an Army Research Office Young Investigator Award in 2000, an Office of Naval Research Young Investigator Award in 2001, and the 2006 IEEE SIGNAL PROCESSING MAGAZINE Best Paper Award. He also received top 10% paper awards at the IEEE Multimedia Signal Processing Workshops in 2011 and 2015. He served as an Associate Editor for five IEEE Transactions. He was the Publications Chair of ICASSP 2007, the Technical Program Committee Co-Chair of ITW 2007, the Tutorial Chair of ISIT 2010, and the Awards Chair of Globecom 2014. He is a fellow of the IEEE.