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.
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. His main research interest lies in image/video processing, networked multimedia, and multi-user information theory. Dr. Xiong received an NSF Career Award in 1999, an ARO Young Investigator Award in 2000, and an ONR Young Investigator Award in 2001. He is co-recipient of the 2006 IEEE Signal Processing Magazine best paper award, top 10% paper awards at the 2011 and 2015 IEEE Multimedia Signal Processing Workshops, and an IBM best student paper award at the 2016 IEEE International Conference on Pattern Recognition. He was the Publications Chair of ICASSP 2007, a Technical Program Committee Co-Chair of ITW 2007, the Tutorial Chair of ISIT 2010, the Awards Chair of Globecom 2014, and a General Co-Chair of MMSP’17. He served as an Associate Editor for five IEEE Transactions. He is currently an associate editor for the IEEE Trans. on Multimedia. He has been a fellow of the IEEE since 2007.