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Selected Topics in Signal Processing, IEEE Journal of

Issue 5 • Date Sept. 2011

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Displaying Results 1 - 22 of 22
  • Table of contents

    Publication Year: 2011 , Page(s): C1
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  • IEEE Journal of Selected Topics in Signal Processing publication information

    Publication Year: 2011 , Page(s): C2
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  • Introduction to the issue on Adaptive Sparse Representation of Data and Applications in Signal and Image Processing

    Publication Year: 2011 , Page(s): 893 - 895
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  • A Review of Adaptive Image Representations

    Publication Year: 2011 , Page(s): 896 - 911
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (5998 KB) |  | HTML iconHTML  

    Improving the modeling of natural images is important to go beyond the state-of-the-art for many image processing tasks such as compression, denoising, inverse problems, and texture synthesis. Natural images are composed of intricate patterns such as regular areas, edges, junctions, oriented oscillations, and textures. Processing efficiently such a wide range of regularities requires methods that ... View full abstract»

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  • Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning

    Publication Year: 2011 , Page(s): 912 - 926
    Cited by:  Papers (54)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1133 KB) |  | HTML iconHTML  

    We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlation and thus their performance degrades significantly with the correlation. In this paper, we propose a block sparse Bayesian learning framework which models t... View full abstract»

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  • Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples

    Publication Year: 2011 , Page(s): 927 - 932
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1431 KB) |  | HTML iconHTML  

    Sparsity is a fundamental concept in compressive sampling of signals/images, which is commonly measured using the l0 norm, even though, in practice, the l1 or the lp ( 0 <; p <; 1) (pseudo-) norm is preferred. In this paper, we explore the use of the Gini index (GI), of a discrete signal, as a more effective measure of its sparsity... View full abstract»

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  • Sparse Approximations for Drum Sound Classification

    Publication Year: 2011 , Page(s): 933 - 940
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (946 KB) |  | HTML iconHTML  

    Up to now, there has only been little work on using features from temporal approximations of signals for audio recognition. Time-frequency tradeoffs are an important issue in signal processing; sparse representations using overcomplete dictionaries may (or may not, depending on the dictionary) have more time-frequency flexibility than standard short-time Fourier transform. Also, the precise tempor... View full abstract»

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  • Learning Sparse Representations of Depth

    Publication Year: 2011 , Page(s): 941 - 952
    Cited by:  Papers (5)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (3301 KB) |  | HTML iconHTML  

    This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, such as that obtained by laser range scanners or structured light depth cameras. Sparse representatio... View full abstract»

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  • Image Recovery Via Hybrid Sparse Representations: A Deterministic Annealing Approach

    Publication Year: 2011 , Page(s): 953 - 962
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (6844 KB) |  | HTML iconHTML  

    Local smoothness and nonlocal similarity have both led to sparsity prior useful to image recovery applications. In this paper, we propose to combine the strengths of local and nonlocal sparse representations by Bayesian model averaging (BMA) where sparsity offers a plausible approximation of model posterior probabilities. An iterative thresholding-based image recovery algorithm using hybrid sparse... View full abstract»

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  • Learning Sparse Codes for Hyperspectral Imagery

    Publication Year: 2011 , Page(s): 963 - 978
    Cited by:  Papers (24)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (2264 KB) |  | HTML iconHTML  

    The spectral features in hyperspectral imagery (HSI) contain significant structure that, if properly characterized, could enable more efficient data acquisition and improved data analysis. Because most pixels contain reflectances of just a few materials, we propose that a sparse coding model is well-matched to HSI data. Sparsity models consider each pixel as a combination of just a few elements fr... View full abstract»

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  • Greedy Dictionary Selection for Sparse Representation

    Publication Year: 2011 , Page(s): 979 - 988
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1419 KB) |  | HTML iconHTML  

    We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse, we mean that only a few dictionary elements, compared to the ambient signal dimension, can exactly represent or well-approximate the signals of interest. We formulate both the selection of the dictionary columns and the ... View full abstract»

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  • Adaptive Sparsity Non-Negative Matrix Factorization for Single-Channel Source Separation

    Publication Year: 2011 , Page(s): 989 - 1001
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1712 KB) |  | HTML iconHTML  

    A novel method for adaptive sparsity non-negative matrix factorization is proposed. The proposed factorization decomposes an information-bearing matrix into two-dimensional convolution of factor matrices that represent the spectral dictionary and temporal codes. We derive a variational Bayesian approach to compute the sparsity parameters for optimizing the matrix factorization. The method is demon... View full abstract»

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  • Restoration of Astrophysical Spectra With Sparsity Constraints: Models and Algorithms

    Publication Year: 2011 , Page(s): 1002 - 1013
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (893 KB) |  | HTML iconHTML  

    We address the problem of joint signal restoration and parameter estimation in the context of the forthcoming MUSE instrument, which will provide spectroscopic measurements of light emitted by very distant galaxies. Restoration of spectra is formulated as a linear inverse problem, accounting for the instrument response and the noise spectral variability. Estimation is considered in the setting of ... View full abstract»

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  • Multi-Scale Dictionary Learning Using Wavelets

    Publication Year: 2011 , Page(s): 1014 - 1024
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1700 KB) |  | HTML iconHTML  

    In this paper, we present a multi-scale dictionary learning paradigm for sparse and redundant signal representations. The appeal of such a dictionary is obvious-in many cases data naturally comes at different scales. A multi-scale dictionary should be able to combine the advantages of generic multi-scale representations (such as Wavelets), with the power of learned dictionaries, in capturing the i... View full abstract»

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  • Fast Dictionary Learning for Sparse Representations of Speech Signals

    Publication Year: 2011 , Page(s): 1025 - 1031
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (404 KB) |  | HTML iconHTML  

    For dictionary-based decompositions of certain types, it has been observed that there might be a link between sparsity in the dictionary and sparsity in the decomposition. Sparsity in the dictionary has also been associated with the derivation of fast and efficient dictionary learning algorithms. Therefore, in this paper we present a greedy adaptive dictionary learning algorithm that sets out to f... View full abstract»

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  • Near-Oracle Performance of Greedy Block-Sparse Estimation Techniques From Noisy Measurements

    Publication Year: 2011 , Page(s): 1032 - 1047
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (576 KB) |  | HTML iconHTML  

    This paper examines the ability of greedy algorithms to estimate a block sparse parameter vector from noisy measurements. In particular, block sparse versions of the orthogonal matching pursuit and thresholding algorithms are analyzed under both adversarial and Gaussian noise models. In the adversarial setting, it is shown that estimation accuracy comes within a constant factor of the noise power.... View full abstract»

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  • Robust and Fast Learning of Sparse Codes With Stochastic Gradient Descent

    Publication Year: 2011 , Page(s): 1048 - 1060
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (3874 KB) |  | HTML iconHTML  

    Particular classes of signals, as for example natural images, can be encoded sparsely if appropriate dictionaries are used. Finding such dictionaries based on data samples, however, is a difficult optimization task. In this paper, it is shown that simple stochastic gradient descent, besides being much faster, leads to superior dictionaries compared to the Method of Optimal Directions (MOD) and the... View full abstract»

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  • Image Compression Using Sparse Representations and the Iteration-Tuned and Aligned Dictionary

    Publication Year: 2011 , Page(s): 1061 - 1073
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (4612 KB) |  | HTML iconHTML  

    We introduce a new image coder which uses the Iteration Tuned and Aligned Dictionary (ITAD) as a transform to code image blocks taken over a regular grid. We establish experimentally that the ITAD structure results in lower-complexity representations that enjoy greater sparsity when compared to other recent dictionary structures. We show that this superior sparsity can be exploited successfully fo... View full abstract»

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  • Image Features Extraction and Fusion Based on Joint Sparse Representation

    Publication Year: 2011 , Page(s): 1074 - 1082
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (5509 KB) |  | HTML iconHTML  

    In this paper, a novel joint sparse representation-based image fusion method is proposed. Since the sensors observe related phenomena, the source images are expected to possess common and innovation features. We use sparse coefficients as image features. The source image is represented with the common and innovation sparse coefficients by joint sparse representation. The sparse coefficients are co... View full abstract»

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  • IEEE Journal of Selected Topics in Signal Processing Information for authors

    Publication Year: 2011 , Page(s): 1083 - 1084
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  • IEEE Signal Processing Society Information

    Publication Year: 2011 , Page(s): C3
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  • Blank page [back cover]

    Publication Year: 2011 , Page(s): C4
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Aims & Scope

The Journal of Selected Topics in Signal Processing (J-STSP) solicits special issues on topics that cover the entire scope of the IEEE Signal Processing Society including the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Fernando Pereira
Instituto Superior Técnico