# Publications

2015
S. H. Chan, T. Zickler, and Y. M. Lu, “Understanding symmetric smoothing filters via Gaussian mixtures,” in IEEE International Conference on Image Processing, 2015.
J. Onativia, P. L. Dragotti, and Y. M. Lu, “Sparsity according to Prony, Average Performance Analysis,” in Signal Processing with Adaptive Sparse Structured Representations (SPARS) Workshop, Cambridge, England, 2015.
C. Hu, et al., “A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer's Disease,” PLOS ONE, vol. 10, no. 5, 2015. Publisher's VersionAbstract

Understanding network features of brain pathology is essential to reveal underpinnings of neurodegenerative diseases. In this paper, we introduce a novel graph regression model (GRM) for learning structural brain connectivity of Alzheimer’s disease (AD) measured by amyloid-β  deposits. The proposed GRM regards 11 C-labeled Pittsburgh Compound-B (PiB) positron emission tomography (PET) imaging data as smooth signals defined on an unknown graph. This graph is then estimated through an optimization framework, which fits the graph to the data with an adjustable level of uniformity of the connection weights. Under the assumed data model, results based on simulated data illustrate that our approach can accurately reconstruct the underlying network, often with better reconstruction than those obtained by both sample correlation and ℓ1 -regularized partial correlation estimation. Evaluations performed upon PiB-PET imaging data of 30 AD and 40 elderly normal control (NC) subjects demonstrate that the connectivity patterns revealed by the GRM are easy to interpret and consistent with known pathology. Moreover, the hubs of the reconstructed networks match the cortical hubs given by functional MRI. The discriminative network features including both global connectivity measurements and degree statistics of specific nodes discovered from the AD and NC amyloid-beta networks provide new potential biomarkers for preclinical and clinical AD.

A. Agaskar and Y. M. Lu, “Optimal Detection of Random Walks on Graphs: Performance Analysis via Statistical Physics,” Technical Report, 2015. arXiv:1504.06924Abstract

We study the problem of detecting a random walk on a graph from a sequence of noisy measurements at every node. There are two hypotheses: either every observation is just meaningless zero-mean Gaussian noise, or at each time step exactly one node has an elevated mean, with its location following a random walk on the graph over time. We want to exploit knowledge of the graph structure and random walk parameters (specified by a Markov chain transition matrix) to detect a possibly very weak signal. The optimal detector is easily derived, and we focus on the harder problem of characterizing its performance through the (type-II) error exponent: the decay rate of the miss probability under a false alarm constraint.
The expression for the error exponent resembles the free energy of a spin glass in statistical physics, and we borrow techniques from that field to develop a lower bound. Our fully rigorous analysis uses large deviations theory to show that the lower bound exhibits a phase transition: strong performance is only guaranteed when the signal-to-noise ratio exceeds twice the entropy rate of the random walk.
Monte Carlo simulations show that the lower bound fully captures the behavior of the true exponent.

2014
A. Agaskar and Y. M. Lu, “Optimal hypothesis testing with combinatorial structure: Detecting random walks on graphs,” in Proc. of Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 2014.Abstract

Suppose we have a time series of observations for each node in a network, and we wish to detect the presence of a particle undergoing a random walk on the network. If there is no such particle, then we observe only zero-mean Gaussian noise. If present, however, the location of the particle has an elevated mean. How well can we detect the particle at low signal-to-noise ratios? This is a special case of the problem of detecting hidden Markov processes (HMPs).

The performance metric we analyze is the error exponent of the optimal detector, which measures the exponential rate of decay in the miss probability if the false alarm probability is held fixed as the observation time increases. This problem exhibits deep connections to a problem in statistical physics: computing the free energy density of a spin glass.

We develop a generalized version of the random energy model (REM) spin glass, whose free energy density provides a lower bound for our error exponent, and compute the bound using large deviations techniques. The bound closely matches empirical results in numerical experiments, and suggests a phase transition phenomenon: below a threshold SNR, the error exponent is nearly constant and near zero, indicating poor performance; above the threshold, there is rapid improvement in performance as the SNR increases. The location of the phase transition depends on the entropy rate of the Markov process.

S. H. Chan and Y. M. Lu, “Efficient image reconstruction for gigapixel quantum image sensors,” in IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, GA, 2014.Abstract

Recent advances in materials, devices and fabrication technologies have motivated a strong momentum in developing solid-state sensors that can detect individual photons in space and time. It has been envisioned that such sensors can eventually achieve very high spatial resolutions (e.g., 10^9 pixels/chip) as well as high frame rates (e.g., 10^6 frames/sec). In this paper, we present an efficient algorithm to reconstruct images from the massive binary bit-streams generated by these sensors. Based on the concept of alternating direction method of multipliers (ADMM), we transform the computationally intensive optimization problem into a sequence of subproblems, each of which has efficient implementations in the form of polyphase-domain filtering or pixel-wise nonlinear mappings. Moreover, we reformulate the original maximum likelihood estimation as maximum a posterior estimation by introducing a total variation prior. Numerical results demonstrate the strong performance of the proposed method, which achieves several dB’s of improvement in PSNR and requires a shorter runtime as compared to standard gradient-based approaches.

A. Agaskar, C. Wang, and Y. M. Lu, “Randomized Kaczmarz algorithms: Exact MSE analysis and optimal sampling probabilities,” in IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, GA, 2014.Abstract

The Kaczmarz method, or the algebraic reconstruction technique (ART), is a popular method for solving large-scale overdetermined systems of equations. Recently, Strohmer et al. proposed the randomized Kaczmarz algorithm, an improvement that guarantees exponential convergence to the solution. This has spurred much interest in the algorithm and its extensions. We provide in this paper an exact formula for the mean squared error (MSE) in the value reconstructed by the algorithm. We also compute the exponential decay rate of the MSE, which we call the “annealed” error exponent. We show that the typical performance of the algorithm is far better than the average performance. We define the “quenched” error exponent to characterize the typical performance. This is far harder to compute than the annealed error exponent, but we provide an approximation that matches empirical results. We also explore optimizing the algorithm’s row-selection probabilities to speed up the algorithm’s convergence.

(This paper received the Best Student Paper Award of GlobalSIP)

J. Oñativia, Y. M. Lu, and P. L. Dragotti, “Finite Dimensional FRI,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, 2014.Abstract

Traditional Finite Rate of Innovation (FRI) theory has considered the problem of sampling continuous-time signals. This framework can be naturally extended to the case where the input is a discrete-time signal. Here we present a novel approach which uses both the traditional FRI sampling scheme, based on the annihilating filter method, and the fact that in this new setup the null space of the problem to be solved is finite dimensional.

In the noiseless scenario, we show that this new approach is able to perfectly recover the original signal at the critical sampling rate. We also present simulation results in the noisy scenario where this new approach improves performances in terms of the mean squared error (MSE) of the reconstructed signal when compared to the canonical FRI algorithms and compressed sensing (CS).

S. H. Chan, T. Zickler, and Y. M. Lu, “Monte Carlo Non-Local Means: Random Sampling for Large-Scale Image Filtering,” IEEE Transactions on Image Processing, vol. 23, no. 8, pp. 3711-3725, 2014.Abstract

We propose a randomized version of the non-local means (NLM) algorithm for large-scale image filtering. The new algorithm, called Monte Carlo non-local means (MCNLM), speeds up the classical NLM by computing a small subset of image patch distances, which are randomly selected according to a designed sampling pattern. We make two contributions. First, we analyze the performance of the MCNLM algorithm and show that, for large images or large external image databases, the random outcomes of MCNLM are tightly concentrated around the deterministic full NLM result. In particular, our error probability bounds show that, at any given sampling ratio, the probability for MCNLM to have a large deviation from the original NLM solution decays exponentially as the size of the image or database grows. Second, we derive explicit formulas for optimal sampling patterns that minimize the error probability bound by exploiting partial knowledge of the pairwise similarity weights. Numerical experiments show that MCNLM is competitive with other state-of-the-art fast NLM algorithms for single-image denoising. When applied to denoising images using an external database containing ten billion patches, MCNLM returns a randomized solution that is within 0.2 dB of the full NLM solution while reducing the runtime by three orders of magnitude.

P. L. Dragotti and Y. M. Lu, “On Sparse Representation in Fourier and Local Bases,” IEEE Transactions on Information Theory, vol. 60, no. 12, pp. 7888-7899, 2014.Abstract

We consider the classical problem of finding the sparse representation of a signal in a pair of bases. When both bases are orthogonal, it is known that the sparse representation is unique when the sparsity $K$ of the signal satisfies $K<1/\mu(\mD)$, where $\mu(\mD)$ is the mutual coherence of the dictionary. Furthermore, the sparse representation can be obtained in polynomial time by Basis Pursuit (BP), when $K<0.91/\mu(\mD)$. Therefore, there is a gap between the unicity condition and the one required to use the polynomial-complexity BP formulation. For the case of general dictionaries, it is also well known that finding the sparse representation under the only constraint of unicity is NP-hard. In this paper, we introduce, for the case of Fourier and canonical bases, a polynomial complexity algorithm that finds all the possible $K$-sparse representations of a signal under the weaker condition that $K<\sqrt{2} /\mu(\mD)$. Consequently, when $K<1/\mu(\mD)$, the proposed algorithm solves the unique sparse representation problem for this structured dictionary in polynomial time. We further show that the same method can be extended to many other pairs of bases, one of which must have local atoms. Examples include the union of Fourier and local Fourier bases, the union of discrete cosine transform and canonical bases, and the union of random Gaussian and canonical bases.

S. Maranò, D. Fäh, and Y. M. Lu, “Sensor Placement for the Analysis of Seismic Surface Waves: Source of Error, Design Criterion, and Array Design Algorithms,” Geophys. J. Int. vol. 197, no. 3, pp. 1566-1581, 2014. Publisher's VersionAbstract

Seismic surface waves can be measured by deploying an array of seismometers on the surface of the earth. The goal of such measurement surveys is, usually, to estimate the velocity of propagation and the direction of arrival of the seismic waves. In this paper, we address the issue of sensor placement for the analysis of seismic surface waves from ambient vibration wavefields. First, we explain in detail how the array geometry affects the mean-squared estimation error (MSEE) of parameters of interest, such as the velocity and direction of propagation, both at low and high signal-to-noise ratios (SNRs). Second, we propose a cost function suitable for the design of the array geometry with particular focus on the estimation of the wavenumber of both Love and Rayleigh waves. Third, we present and compare several computational approaches to minimize the proposed cost function. Numerical experiments verify the effectiveness of our cost function and resulting array geometry designs, leading to greatly improved estimation performance in comparison to arbitrary array geometries, both at low and high SNR levels.

2013
Y. M. Lu, “A Framework for Adaptive Parameter Estimation with Finite Memory,” in Proc. IEEE Global Conference on Signal and Information Processing, Austin, TX, 2013.Abstract
We consider the problem of estimating an unknown parameter from a finite collection of different statistical experiments. The measurements are taken sequentially. Based on the observations made so far, we adaptively select the next experiment that provides the most information about the parameter. Summarizing past information with finite memory, we present a general framework for efficient adaptive estimation, with the sensing schemes fully characterized by finite-state parametric Markov chains. We establish an analytic formula linking the asymptotic performance of adaptive estimation schemes to the steady-state distributions of the associated Markov chains. Consequently, finding optimal adaptive strategies can be reformulated as the problem of designing a (continuous) family of Markov chains with prescribed steady-state distributions.We also propose a quantitative design criterion for optimal sensing policies based on minimax ratio regret.
A. Agaskar and Y. M. Lu, “ALARM: A Logistic Auto-Regressive Model for binary processes on networks,” in Proc. IEEE Global Conference on Signal and Information Processing, Austin, TX, 2013.Abstract

We introduce the ALARM model, a logistic autoregressive model for discrete-time binary processes on networks, and describe a technique for learning the graph structure underlying the model from observations. Using only a small number of parameters, the proposed ALARM can describe a wide range of dynamic behavior on graphs, such as the contact process, voter process, and even some epidemic processes. Under ALARM, at each time step, the probability of a node having value 1 is determined by the values taken by its neighbors in the past; specifically, its probability is given by the logistic function evaluated at a linear combination of its neighbors' past values (within a fixed time window) plus a bias term. We examine the behavior of this model for 1D and 2D lattice graphs, and observe a phase transition in the steady state for 2D lattices. We then study the problem of learning a graph from ALARM observations. We show how a regularizer promoting group sparsity can be used to efficiently learn the parameters of the model from a realization, and demonstrate the resulting ability to reconstruct the underlying network from the data.

Z. Sadeghipoor, Y. M. Lu, and S. Susstrunk, “A novel compressive sensing approach to simultaneously acquire color and near-infrared images on a single sensor,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada, 2013.Abstract

Sensors of most digital cameras are made of silicon that is inherently sensitive to both the visible and near-infrared parts of the electromagnetic spectrum. In this paper, we address the problem of color and NIR joint acquisition. We propose a framework for the joint acquisition that uses only a single silicon sensor and a slightly modified version of the Bayer color-filter array that is already mounted in most color cameras. Implementing such a design for an RGB and NIR joint acquisition system requires minor changes to the hardware of commercial color cameras. One of the important differences between this design and the conventional color camera is the post-processing applied to the captured values to reconstruct full resolution images. By using a CFA similar to Bayer, the sensor records a mixture of NIR and one color channel in each pixel. In this case, separating NIR and color channels in different pixels is equivalent to solving an under-determined system of linear equations. To solve this problem, we propose a novel algorithm that uses the tools developed in the field of compressive sensing. Our method results in high-quality RGB and NIR images (the average PSNR of more than 30 dB for the reconstructed images) and shows a promising path towards RGB and NIR cameras.

S. H. Chan, T. Zickler, and Y. M. Lu, “Fast non-local filtering by random sampling: It works, especially for large images,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada, 2013.Abstract

Non-local means (NLM) is a popular denoising scheme. Conceptually simple, the algorithm is computationally intensive for large images. We propose to speed up NLM by using random sampling. Our algorithm picks, uniformly at random, a small number of columns of the weight matrix, and uses these representatives'' to compute an approximate result. It also incorporates an extra column-normalization of the sampled columns, a form of symmetrization that often boosts the denoising performance on real images. Using statistical large deviation theory, we analyze the proposed algorithm and provide guarantees on its performance. We show that the probability of having a large approximation error decays exponentially as the image size increases. Thus, for large images, the random estimates generated by the algorithm are tightly concentrated around their limit values, even if the sampling ratio is small. Numerical results confirm our theoretical analysis: the proposed algorithm reduces the run time of NLM, and thanks to the symmetrization step, actually provides some improvement in peak signal-to-noise ratios.

A. Agaskar and Y. M. Lu, “Detecting random walks hidden in noise: Phase transition on large graphs,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada, 2013.Abstract

We consider the problem of distinguishing between two hypotheses: that a sequence of signals on a large graph consists entirely of noise, or that it contains a realization of a random walk buried in the noise. The problem of computing the error exponent of the optimal detector is simple to formulate, but exhibits deep connections to problems known to be difficult, such as computing Lyapunov exponents of products of random matrices and the free entropy density of statistical mechanical systems. We describe these connections, and define an algorithm that efficiently computes the error exponent of the Neyman-Pearson detector. We also derive a closed-form formula, derived from a statistical mechanics-based approximation, for the error exponent on an arbitrary graph of large size. The derivation of this formula is not entirely rigorous, but it closely matches the empirical results in all our experiments. This formula explains a phase transition phenomenon in the error exponent: below a threshold SNR, the error exponent is nearly constant and near zero, indicating poor performance; above the threshold, there is rapid improvement in performance as the SNR increases. The location of the phase transition depends on the entropy rate of the random walk.

I. Dokmanic, R. Parhizkar, A. Walther, Y. M. Lu, and M. Vetterli, “Acoustic Echoes Reveal Room Shape,” Proceedings of the National Academy of Sciences (PNAS), vol. 110, no. 30, pp. 12186-12191, 2013. Full Text (PDF + Supplementary Info)Abstract
Imagine that you are blindfolded inside an unknown room. You snap your fingers and listen to the room’s response. Can you hear the shape of the room? Some people can do it naturally, but can we design computer algorithms that hear rooms? We show how to compute the shape of a convex polyhedral room from its re- sponse to a known sound, recorded by a few microphones. Geo- metric relationships between the arrival times of echoes enable us to “blindfoldedly” estimate the room geometry. This is achieved by exploiting the properties of Euclidean distance matrices. Fur- thermore, we show that under mild conditions, first-order echoes provide a unique description of convex polyhedral rooms. Our algorithm starts from the recorded impulse responses and pro- ceeds by learning the correct assignment of echoes to walls. In contrast to earlier methods, the proposed algorithm reconstructs the full 3D geometry of the room from a single sound emission, and with an arbitrary geometry of the microphone array. As long as the microphones can hear the echoes, we can position them as we want. Besides answering a basic question about the inverse problem of room acoustics, our results find applications in areas such as architectural acoustics, indoor localization, virtual reality, and audio forensics.
Y. M. Lu, “Adaptive sensing and inference for single-photon imaging,” in Proc. 47th Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, 2013.Abstract

In recent years, there have been increasing efforts to develop solid-state sensors with single-photon sensitivity, with applications ranging from bio-imaging to 3D computer vision. In this paper, we present adaptive sensing models, theory and algorithms for these single-photon sensors, aiming to improve their dynamic ranges. Mapping different sensor configurations onto a finite set of states, we represent adaptive sensing schemes as finite-state parametric Markov chains. After deriving an asymptotic expression for the Fisher information rate of these Markovian systems, we propose a design criterion for sensing policies based on minimax ratio regret. We also present a suboptimal yet effective sensing policy based on random walks. Numerical experiments demonstrate the strong performance of the proposed scheme, which expands the sensor dynamic ranges of existing nonadaptive approaches by several orders of magnitude.

C. Hu, L. Cheng, J. Sepulcre, G. E. Fakhri, Y. M. Lu, and Q. Li, “Matched signal detection on graphs: Theory and application to brain network classification,” in Proc. 23rd International Conference on Information Processing in Medical Imaging (IPMI 2013), Asilomar, CA, 2013.Abstract
We develop a matched signal detection (MSD) theory for signals with an intrinsic structure described by a weighted graph. Hypothesis tests are formulated under different signal models. In the simplest scenario, we assume that the signal is deterministic with noise in a subspace spanned by a subset of eigenvectors of the graph Laplacian. The conventional matched subspace detection can be easily extended to this case. Furthermore, we study signals with certain level of smoothness. The test turns out to be a weighted energy detector, when the noise variance is negligible. More generally, we presume that the signal follows a prior distribution, which could be learnt from training data. The test statistic is then the difference of signal variations on associated graph structures, if an Ising model is adopted. Effectiveness of the MSD on graph is evaluated both by simulation and real data. We apply it to the network classification problem of Alzheimer’s disease (AD) particularly. The preliminary results demonstrate that our approach is able to exploit the sub-manifold structure of the data, and therefore achieve a better performance than the traditional principle component analysis (PCA).
C. Hu, L. Cheng, J. Sepulcre, G. E. Fakhri, Y. M. Lu, and Q. Li, “A graph theoretical regression model for brain connectivity learning of Alzheimer's disease,” in Proc. International Symposium on Biomedical Imaging (ISBI), San Francisco, CA, 2013.Abstract

Learning functional brain connectivity is essential to the understanding of neurodegenerative diseases. In this paper, we introduce a novel graph regression model (GRM) which regards the imaging data as signals defined on a graph and optimizes the fitness between the graph and the data, with a sparsity level regularization. The proposed framework features a nice interpretation in terms of low-pass signals on graphs, and is more generic compared with previous statistical models. Results based on the data illustrates that our approach can obtain a very close reconstruction of the true network. We then apply the GRM to learn the brain connectivity of Alzheimer’s disease (AD). Evaluations performed upon PET imaging data of 30 AD patients demonstrate that the connectivity patterns discovered are easy to interpret and consistent with known pathology.