33 Oxford Street, Maxwell-Dworkin Laboratory, Rm 321, Harvard University, Cambridge, MA 02138.
schan AT seas DOT harvard DOT edu
(email)
I am a post-doctoral research fellow at Harvard School of Engineering and Applied Science, under the support of Croucher Foundation post-doctoral research fellowship (2012-2013).
My current research focuses on statistical signal processing for large data. I am interested in designing and analyzing tools for signal reconstruction, inference and detection. In particular, I work on:
My advisors at Harvard are Todd Zickler and Yue M. Lu.
You are welcome to visit my old website at UC San Diego: http://videoprocessing.ucsd.edu/~stanleychan
DECONVTV is a fast numerical optimization package for solving total variation image and video deconvolution problems. It is now available for non-commercial research purposes. For details, please check the software page.
 | deconvtv_v1.zip | 567.02 KB |
Our paper "Fast non-local filtering by random sampling" is also accepted for poster presentation in IEEE International Conference on Computational Photography (ICCP 2013).
A new paper "Fast non-local filtering by random sampling" is accepted to IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 2013). In this paper, Yue and I applied large deviation principles (LDP) to study a very popular class of image denoising algorithms known as the non-local filtering. We showed that with extremely high probability, the computationally intensive non-local filtering operations can be approximated using a Monte Carlo random sampling scheme. In addition, we found that with one additional cost-less step by normalizing the columns of the graph adjacency matrix, the asymmetric non-local filter becomes symmetrized according to Sinkhorn-Knopp. This symmetrized filter further boosts up denoising quality by a big margin.
 | chan_zickler_lu_2013.pdf | 257.64 KB |