IMPORTANT: In Fall 2014 I will be joining Purdue University as an assistant professor in Electrical Engineering and Statistics. Please visit the new page HERE.

I am a post-doctoral research fellow at Harvard School of Engineering and Applied Science, under the support of the Croucher Foundation Post-Doctoral Research Fellowship.

My current research focuses on statistical signal processing for large-scale data, with long term efforts in developing theories and algorithms to recover latent structures and processes leveraging the data. Topics of interest include signal reconstruction, sampling theory, network analysis and numerical optimization. In particular, I work on the following projects:

  • Imaging:
    • (1) Random Sampling for Image Filtering
    • (2) Single-Photon Imaging Sensors
  • Network Analysis:
    • (1) Nonparametric Graphon Estimation by Stochastic Block Approximation
    • (2) Total Variation based Graphon Estimation Algorithms

My advisors at Harvard SEAS are Yue M. Lu and Todd Zickler. I also work with Edo Airoldi at the Department of Statistics.

You are welcome to visit my old website at UC San Diego:


``Sparse Reconstruction of Depth Data: Representation, Algorithm and Sampling'' is submitted to IEEE Trans. Image Process.

July 25, 2014

The rapid development of 3D technology and computer vision applications have motivated a thrust of methodologies for depth acquisition and estimation. However, most existing hardware and software methods have limited performance due to poor depth precision, low resolution and high computational cost. In this paper, we present a computationally efficient method to recover dense depth maps from sparse measurements. We make three contributions. First, we provide empirical evidence that depth maps can be encoded much more sparsely than natural images by using common dictionaries such as wavelets and contourlets. We also show that a combined wavelet-contourlet dictionary achieves better performance than using either dictionary alone. Second, we propose an alternating direction method of multipliers (ADMM) to achieve fast reconstruction. A multi-scale warm start procedure is proposed to speed up the convergence. Third, we propose a two-stage randomized sampling scheme to optimally choose the sampling locations, thus maximizing the reconstruction performance for any given sampling budget. Experimental results show that the proposed method produces high quality dense depth estimates, and is robust to noisy measurements. Applications to real data in stereo matching are demonstrated.

Paper is available at

``Adaptive Image Denoising by Targeted Databases'' is submitted to IEEE Trans. Image Process.

July 25, 2014

We propose a data-dependent denoising procedure to restore noisy images. Different from existing denoising algorithms which search for patches from either the noisy image or a generic database, the new algorithm finds patches from a database that contains only relevant patches. We formulate the denoising problem as an optimal filter design problem and make two contributions. First, we determine the basis function of the denoising filter by solving a group sparsity minimization problem. The optimization formulation generalizes existing denoising algorithms and offers systematic analysis of the performance. Improvement methods are proposed to enhance the patch search process. Second, we determine the spectral coefficients of the denoising filter by considering a localized Bayesian prior. The localized prior leverages the similarity of the targeted database, alleviates the intensive Bayesian computation, and links the new method to the classical linear minimum mean squared error estimation. We demonstrate applications of the proposed method in a variety of scenarios, including text images, multiview images and face images. Experimental results show the superiority of the new algorithm over existing methods.

Paper is available at:

"Image Denoising by Targeted External Databases" is accepted to ICASSP 2014

February 8, 2014

Classical image denoising algorithms based on single noisy images and generic image databases will soon reach their performance limits. In this paper, we propose to denoise images using targeted external image databases. Formulating denoising as an optimal filter design problem, we utilize the targeted databases to (1) determine the basis functions of the optimal filter by means of group sparsity; (2) determine the spectral coefficients of the optimal filter by means of localized priors. For a variety of scenarios such as text images, multiview images, and face images, we demonstrate superior denoising results over existing algorithms.

Enming Luo, Stanley H. Chan, and Truong Q. Nguyen, “Image Denoising by Targeted External Databases”, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2014, pp.3019-3023. (Awarded ICASSP Student Travel Grant)