Guangyong Chen

An Efficient Statistical Method for Image Noise Level Estimation

In this paper, we address the problem of estimating noise level from a single image contaminated by additive zero-mean Gaussian noise. We first provide rigorous analysis on the statistical relationship between the noise variance and the eigenvalues of the covariance matrix of patches within an image, which shows that many state-of-the-art noise estimation methods underestimate the noise level of an image. To this end, we derive a new nonparametric algorithm for efficient noise level estimation based on the observation that patches decomposed from a clean image often lie around a low-dimensional subspace. The performance of our method has been guaranteed both theoretically and empirically. Specifically, our method outperforms existing state-of-the-art algorithms on estimating noise level with the least executing time in our experiments. We further demonstrate that the denoising algorithm BM3D algorithm achieves optimal performance using noise variance estimated by our algorithm.

From Noise Modeling to Blind Image Denoising

Traditional image denoising algorithms always assume the noise to be homogeneous white Gaussian distributed. However, the noise on real images can be much more complex empirically. This paper addresses this problem and proposes a novel blind image denoising algorithm which can cope with real-world noisy images even when the noise model is not provided. It is realized by modeling image noise with mixture of Gaussian distribution (MoG) which can approximate large varieties of continuous distributions. As the number of components for MoG is unknown practically, this work adopts Bayesian nonparametric technique and proposes a novel Low-rank MoG filter (LR-MoG) to recover clean signals (patches) from noisy ones contaminated by MoG noise. Based on LR-MoG, a novel blind image denoising approach is developed. To test the proposed method, this study conducts extensive experiments on synthesis and real images. Our method achieves the state-of the-art performance consistently.

Projection-embedded BYY learning algorithm for Gaussian mixture-based clustering

On learning the Gaussian mixture model, existing BYY learning algorithms are featured by a gradient-based line search with an appropriate stepsize. Learning becomes either unstable if the stepsize is too large or slow and gets stuck in a local optimal solution if the stepsize is too small. An algorithm without a learning stepsize has been proposed with expectation-maximization (EM) like two alternative steps. However, its learning process may still be unstable. This paper tackles this problem of unreliability by a modified algorithm called projection-embedded Bayesian Ying-Yang learning algorithm (pBYY). Experiments have shown that pBYY outperforms learning algorithms developed from not only minimum message length with Jeffreys prior (MML-Jef) and Variational Bayesian with Dirichlet-Normal-Wishart (VB-DNW) prior but also BYY with these priors (BYY-Jef and BYY-DNW). pBYY obtains the superiority with an easy implementation, while DNW prior-based learning algorithms suffer a complicated and tedious computation load. The performance of pBYY has also been demonstrated on the Berkeley Segmentation Dataset for the topic of unsupervised image segmentation. The resulted performances of semantic image segmentation have shown that pBYY outperforms not only MML-Jef, VB-DNW, BYY-Jef, and BYY-DNW but also three leading image segmentation algorithms, namely gPb-owt-ucm, MN-Cut, and mean shift.

Blind Image Denoising via Dependent Dirichlet Process Tree

Most existing image denoising approaches assumed the noise to be homogeneous white Gaussian distributed with known intensity. However, in real noisy images, the noise models are usually unknown beforehand and can be much more complex. This paper addresses this problem and proposes a novel blind image denoising algorithm to recover the clean image from noisy one with the unknown noise model. To model the empirical noise of an image, our method introduces the mixture of Gaussian distribution, which is flexible enough to approximate different continuous distributions. The problem of blind image denoising is reformulated as a learning problem. The procedure is to first build a two-layer structural model for noisy patches and consider the clean ones as latent variable. To control the complexity of the noisy patch model, this work proposes a novel Bayesian nonparametric prior called “Dependent Dirichlet Process Tree” to build the model. Then, this study derives a variational inference algorithm to estimate model parameters and recover clean patches. We apply our method on synthesis and real noisy images with different noise models. Comparing with previous approaches, ours achieves better performance. The experimental results indicate the efficiency of the proposed algorithm to cope with practical image denoising tasks.

Large-Scale Bayesian Probabilistic Matrix Factorization with Memo-Free Distributed Variational Inference

Bayesian Probabilistic Matrix Factorization (BPMF) is a powerful model in many dyadic data prediction problems, especially the applications of Recommender system. However, its poor scalability has limited its wide applications on massive data. Based on the conditional independence property of observed entries in BPMF model, we propose a novel distributed memo-free variational inference method for large-scale matrix factorization problems. Compared with the state-of-the-art methods, the proposed method is favored for several attractive properties. Specifically, it does not require tuning of learning rate carefully, shuffling the training set at each iteration, or storing massive redundant variables, and can introduce new agents into the computations on the fly. We conduct extensive experiments on both synthetic and real-world datasets. The experimental results show that our method can converge significantly faster with better prediction performance than alternative algorithms.

Tracking topology structure adaptively with deep neural networks

Object tracking still remains challenging in computer vision because of the severe object variation, e.g., deformation, occlusion, and rotation. To handle the object variation and achieve robust object tracking performance, we propose a novel relationship-based tracking algorithm using neural networks in this paper. Compared with existing approaches in the literature, our method assumes the targeted object to be consisted of several parts and considers the evolution of the topology structure among these parts. After training a candidate neural network for predicting the probable areas each part may locate at in the successive frame, we then design a novel collaboration neural network to determine the precise area each part will locate at with account for the topology structure among these individual parts, which is learned from their historical physical locations during online tracking process. Experimental results show that the proposed method outperforms state-of-the-art trackers on a benchmark dataset, yielding the significant improvements in accuracy on high-distorted sequences.

A Bayesian Nonparametric Approach to Dynamic Dyadic Data Prediction

An important issue of using matrix factorization for recommender systems is to capture the dynamics of user preference over time for more accurate prediction. We find that considering the existence of clusters among users with respect to evolution behavior of their preference can improve performance effectively. This is especially important to commercial recommender systems, where the evolution of preference for different users is heterogeneous, and historical ratings are not enough to estimate the preference of each user individually. Based on this, we propose a novel Bayesian nonparametric method based on the Dirichlet process, to detect users sharing the same evolution behavior of their preference. For each community, we use vector autoregressive model (VAR) to capture the evolution to explore higher-order dependency on historical user preference, and incorporate this feature with a novel adaptive prior strategy. We also derive variational inference approach to infer our method. Finally, we conduct extensive empirical experiments to show the advantage of our method over state-of-the-art algorithms.

Image denoising, local factor analysis, Bayesian Ying-Yang harmony learning

Abstract A new nonlocal-filtering method LFA-BYY is proposed for image demonizing via learning a local factor analysis (LFA) model from a polluted image under processing and then denoising the image by the learned LFA model. With the help of the Bayesian Ying-Yang (BYY) harmony learning, LFA-BYY can appropriately control the dictionary complexity and learn the noise intensity from the present image under processing, while the existing state-of-the-art methods either use a pretrained dictionary or a general basis, and require an accurate noise intensity estimation provided in advance. In comparison with BM3D, K-SVD, EPLL, and Msi on the benchmark Kodak dataset and additional medical data, experiments have shown that LFA-BYY has not only obtained competitive results on images polluted by a small noise but also outperformed these competing methods when the noise intensity increases beyond a point, especially with significant improvements as the noise intensity becomes large.