A new paper "Adaptive Non-local Means for Multiview Image Denoising - Searching for the Right Patches via a Statistical Approach" is accepted to IEEE International Conference on Image Processing (ICIP 2013). In this paper, me and my colleagues at UC San Diego propose an adaptive denoising scheme for multiview imaging systems. Different from the classical non-local means where the number of patches is fixed, we propose to adaptively choose the best patches for denoising. For more details of this project, please visit http://videoprocessing.ucsd.edu/~eluo/projects/multiview_denoising/

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. 

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.