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IEEE Statistical Signal Processing Workshop (SSP)

作者： Wang, Ren Liao, Xuejun Guo, Jingbo Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

ISBN: (纸本)9781538615713

Gaussian mixture models (GMM) have been used to statistically represent patches in an image. Extending from small patches to an entire image, we propose a convolutional Gaussian mixture models (convGMM) to model the statistics of an entire image and apply it for compressive sensing (CS). We present the algorithm details for learning a convGMM from training images by maximizing the marginal log-likelihood estimation (MMLE). The learned convGMM is used to perform model-based compressive sensing, using the convGMM as a model of the underlying image. In addition, a key feature of our method is that all of the training and reconstruction process could be fast and efficient calculated in the frequency-domain by 2-dimensional fast Fourier transforms (2d-FFTs). The performance of the convGMM on CS is demonstrated on several image sets.

关键词： Gaussian Mixture Models (GMM) Bayesian compressive sensing convolutional sparse coding expectation maximization (EM)

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Nonlinear models for neurophysiological time series

Nonlinear models for neurophysiological time series

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作者： Tom Dupre La Tour Universite Paris-Saclay

学位级别：博士

Brain analysis mainly relies on complex recording techniques and on advanced signal processing tools used to interpret these recordings. In neurophysiological time series, as strong neural oscillations are observed in the mammalian brain, the natural processing tools are centered on spectral analysis, Fourier decomposition, and on linear filtering into canonical frequency bands. While this approach has had significant impact in neuroscience, it may give a misleading representation of the signal. Indeed, it is standard to see neuro-scientists consider small subsets of coefficients, implicitly assuming that the signals of interest are narrow-band, which turns out to be too reductive. Multiple warnings have been raised about this fallacy, and about the need of more appropriate methods to represent the signals. More generally, a large number of neuroscientific studies use ad-hoc recipes to analyze time series and describe their properties. Importantly, these methods are heavily based on narrow-band filtering and on custom correlation metrics, and they fail to give a goodness of fit. Therefore, setting the parameters of these methods can only be driven by how much they lead to a strong value of the metric. As a consequence, even though these metrics give reasonable information, a legitimate and controlled comparison of methods and parameters, and therefore of the results, is impossible. This is the case for instance for a phenomenon known as phase-amplitude coupling (PAC), which consists in an amplitude modulation of a high frequency signal, time-locked with a specific phase of a slow neural oscillation. In this work, we first propose to use driven autoregressive models (DAR) on neuro- physiological time-series. These models give a spectral representation of the signal conditionally to another signal, and thus are able to capture PAC in a probabilistic model of the signal, for which statistical inference is fast and well-posed. Giving a proper model to the signal enables

关键词： Time series modeling autoregressive convolutional sparse coding cross-frequency coupling multivariate encoding neurophysiology magneto-encephalography

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A two-stage convolutional sparse prior model for image restoration

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2017年第Oct.期48卷 268-280页

作者： Xiong, Jiaojiao Liu, Qiegen Wang, Yuhao Xu, Xiaoling Nanchang Univ Dept Elect Informat Engn Nanchang Jiangxi Peoples R China

Image restoration (IR) from noisy, blurred or/and incomplete observed measurement is one of the important tasks in image processing community. Image prior is of utmost importance for recovering a high quality image. In this paper, we present a two-stage convolutional sparse prior model for efficient image restoration. The multi-view features prior is first obtained by convolving the image with the Fields-of Experts (FoE) filters and then the resulting multi-view features are represented by convolutional sparse coding (CSC) prior. By taking advantage of the convolutional filters, the proposed two-stage model inherits the strengths of multi-view features and CSC priors. The assembled multi-view features contain high frequency, redundancy, and large range of feature orientations, which are favor to be represented by CSC and consequently for better image recovery. Augmented Lagrangian and alternating direction method of multipliers are employed to decouple the nonlinear optimization problem in order to iteratively approach the optimum solution. The results of various experiments on image deblurring and compressed sensing magnetic resonance imaging (CS-MRI) reconstruction consistently demonstrate that the proposed algorithm efficiently recovers image and presents advantages over the current leading restoration approaches. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Image deblurring CS-MRI Fields-of-Experts convolutional sparse coding Multi-view features prior Alternating direction method of multipliers

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sparse Overcomplete Denoising: Aggregation Versus Global Optimization

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IEEE SIGNAL PROCESSING LETTERS 2017年第10期24卷 1468-1472页

作者： Carrera, Diego Boracchi, Giacomo Foi, Alessandro Wohlberg, Brendt Politecn Milan I-20133 Milan Italy Tampere Univ Technol Tampere 33720 Finland Los Alamos Natl Lab Los Alamos NM 87545 USA

Denoising is often addressed via sparse coding with respect to an overcomplete dictionary. There are two main approaches when the dictionary is composed of translates of an orthonormal basis. The first, traditionally employed by techniques such as wavelet cycle spinning, separately seeks sparsity w.r.t. each translate of the orthonormal basis, solving multiple partial optimizations and obtaining a collection of sparse approximations of the noise-free image, which are aggregated together to obtain a final estimate. The second approach, recently employed by convolutional sparse representations, instead seeks sparsity over the entire dictionary via a global optimization. It is tempting to view the former approach as providing a suboptimal solution of the latter. In this letter, we analyze whether global sparsity is a desirable property, and under what conditions the global optimization provides a better solution to the denoising problem. In particular, our experimental analysis shows that the two approaches attain comparable performance in case of natural images and global optimization outperforms the simpler aggregation of partial estimates only when the image admits an extremely sparse representation. We explain this phenomenon by separately studying the bias and variance of these solutions, and by noting that the variance of the global solution increases very rapidly as the original signal becomes less and less sparse.

关键词： convolutional sparse coding denoising overcomplete representations sparse representations

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convolutional sparse Representations with Gradient Penalties

Convolutional Sparse Representations with Gradient Penalties

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Brendt Wohlberg Theoretical Division Los Alamos National Laboratory Los Alamos NM 87545 USA

ISBN: (纸本)9781538646595

While convolutional sparse representations enjoy a number of useful properties, they have received limited attention for image reconstruction problems. The present paper compares the performance of block-based and convolutional sparse representations in the removal of Gaussian white noise. The usual formulation of the convolutional sparse coding problem is slightly inferior to the block-based representations in this problem, but the performance of the convolutional form can be boosted beyond that of the block-based form by the inclusion of suitable penalties on the gradients of the coefficient maps.

关键词： convolutional sparse Representations convolutional sparse coding Total Variation

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SYNTHESIS-ANALYSIS DEconvolutional NETWORK FOR COMPRESSED SENSING 24

SYNTHESIS-ANALYSIS DECONVOLUTIONAL NETWORK FOR COMPRESSED SE...

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24th IEEE International Conference on Image Processing (ICIP)

作者： Liu, Qiegen Leung, Henry Nanchang Univ Sch Elect Informat Engn Nanchang 330031 Jiangxi Peoples R China Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

ISBN: (纸本)9781509021758

Synthesis learning and analysis learning, with sparse coding (SC) and Markov random fields (MRFs) as two representative types of models, are two complementary tools to describe the image manifolds. SC has strengths in representing the regular features/explicit visual manifolds while its effectiveness depends on the training dataset. While MRFs have great potentials to characterize the stochastic textures/implicit visual manifolds but at the cost of high training complexity. In this paper, by means of the convolutional operator, a unified synthesis and analysis deconvolutional network (SADN) is presented. It not only requires the generative coding coefficients to be sparse, but also enforces the convolution between the filter and trained images to be sparse. The proposed model incorporates the strengths of both SC and MRFs, which enables it to represent general images with both generative and discriminative abilities. The resulting minimization is tackled by the combination of alternating optimization and Iterative Re-weighted Least Square (IRLS). Experiments conducted on compressed sensing (CS) application show its great potentials both quantitatively and qualitatively.

关键词： convolutional sparse coding Analysis learning MRFs Deconvolutional network CS recovery

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LEVERAGING 2D AND 3D CUES FOR FINE-GRAINED OBJECT CLASSIFICATION 23

LEVERAGING 2D AND 3D CUES FOR FINE-GRAINED OBJECT CLASSIFICA...

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23rd IEEE International Conference on Image Processing (ICIP)

作者： Wang, Xiaolong Li, Robert (Bo) Currey, Jon Samsung Res Amer Mountain View CA 94043 USA

ISBN: (纸本)9781467399616

Objects in fine-grained categories always share a high degree of shape similarity, making both "localizing discriminative parts" and "learning appearance descriptors" extremely difficult. We propose a framework to leverage 2D+3D cues to handle above two challenges. Towards the goal of image alignment to localize discriminative parts, traditional methods rely on either manual part annotation or image segmentation. Instead, our framework leverages each image's 3D camera pose estimation to align images;Towards the goal of "learning appearance descriptors" confined with small training data and memory/computation cost, we propose an unsupervised convolutional sparse coding (CSC) + manifold learning that significantly reduces model complexity, but still successfully produces highly diverse feature filters like deep neural network. Our experimental results attest the advocated framework's accuracy is comparable to a deep network, demonstrating its great potential on mobile devices.

关键词： Fine-grained object classification 2D+3D cues convolutional sparse coding

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convolutional sparse REPRESENTATIONS AS AN IMAGE MODEL FOR IMPULSE NOISE RESTORATION 12

CONVOLUTIONAL SPARSE REPRESENTATIONS AS AN IMAGE MODEL FOR I...

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12th IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)

作者： Wohlberg, Brendt Los Alamos Natl Lab Div Theoret Los Alamos NM 87545 USA

ISBN: (纸本)9781509019298

Standard sparse representations, applied independently to a set of overlapping image blocks, are a very effective approach to a wide variety of image reconstruction problems. convolutional sparse representations, which provide a single-valued representation optimised over an entire image, provide an alternative form of sparse representation that has recently started to attract interest for image reconstruction problems. The present paper provides some insight into the suitability of the convolutional form for this type of application by comparing its performance as an image model with that of the standard model in an impulse noise restoration problem.

关键词： sparse Representation convolutional sparse coding Salt-and-Pepper Noise

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BOUNDARY HANDLING FOR convolutional sparse REPRESENTATIONS 23

BOUNDARY HANDLING FOR CONVOLUTIONAL SPARSE REPRESENTATIONS

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23rd IEEE International Conference on Image Processing (ICIP)

作者： Wohlberg, Brendt Los Alamos Natl Lab Div Theoret Los Alamos NM 87545 USA

ISBN: (纸本)9781467399616

convolutional sparse representations differ from the standard form in representing the signal to be decomposed as the sum of a set of convolutions with dictionary filters instead of a linear combination of dictionary vectors. The advantage of the convolutional form is that it provides a single-valued representation optimised over an entire signal. The substantial computational cost of the convolutional sparse coding and dictionary learning problems has recently been shown to be greatly reduced by solving in the frequency domain, but the periodic boundary conditions imposed by this approach have the potential to create boundary artifacts. The present paper compares different approaches to avoiding these effects in both sparse coding and dictionary learning.

关键词： convolutional sparse coding convolutional Dictionary Learning Boundary Effects

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LEVERAGING 2D AND 3D CUES FOR FINE-GRAINED OBJECT CLASSIFICATION

LEVERAGING 2D AND 3D CUES FOR FINE-GRAINED OBJECT CLASSIFICA...

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IEEE International Conference on Image Processing

作者： Xiaolong Wang Robert (Bo) Li Jon Currey Samsung Research America

ISBN: (纸本)9781467399623

关键词： Fine-grained object classification 2D+3D cues convolutional sparse coding image registration Frameworks Leverage Mobile devices Taxonomy image segmentation Three Dimensions train data sparse coding

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