Sparse Image and Signal Processing Second Edition

Sparse Image and Signal Processing  Second Edition

This thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing.

Author: Jean-Luc Starck

Publisher:

ISBN: OCLC:1137342458

Category:

Page: 444

View: 935

This thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB® and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications.
Categories:

Sparse Image and Signal Processing

Sparse Image and Signal Processing

... Sparse approximation via iterative thresholding, in IEEE Conference on
Acoustics, Speech and Signal Processing, vol. ... promoting seismic image
recovery with curvelet frames, Applied and Computational Harmonic Analysis 24,
150–173.

Author: Jean-Luc Starck

Publisher: Cambridge University Press

ISBN: 9781139642835

Category: Computers

Page:

View: 585

This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Recent concepts of sparsity and morphological diversity are described and exploited for various problems such as denoising, inverse problem regularization, sparse signal decomposition, blind source separation, and compressed sensing. This book weds theory and practice in examining applications in areas such as astronomy, biology, physics, digital media, and forensics. A final chapter explores a paradigm shift in signal processing, showing that previous limits to information sampling and extraction can be overcome in very significant ways. Matlab and IDL code accompany these methods and applications to reproduce the experiments and illustrate the reasoning and methodology of the research are available for download at the associated web site.
Categories: Computers

Sparse Image and Signal Processing

Sparse Image and Signal Processing

Presents the state of the art in sparse and multiscale image and signal processing.

Author: Jean-Luc Starck

Publisher:

ISBN: 1107203430

Category: Electronic books

Page: 316

View: 635

Presents the state of the art in sparse and multiscale image and signal processing.
Categories: Electronic books

Sparse Image and Signal Processing

Sparse Image and Signal Processing

Presents the state of the art in sparse and multiscale image and signal processing.

Author: Jean-Luc Starck

Publisher:

ISBN: 1139635557

Category: Image processing

Page: 316

View: 156

Presents the state of the art in sparse and multiscale image and signal processing.
Categories: Image processing

Sparse Image and Signal Processing

Sparse Image and Signal Processing

Presents state-of-the-art sparse and multiscale image and signal processing with applications in astronomy, biology, MRI, media, and forensics.

Author: Jean-Luc Starck

Publisher: Cambridge University Press

ISBN: 9781107088061

Category: Computers

Page: 428

View: 123

Presents state-of-the-art sparse and multiscale image and signal processing with applications in astronomy, biology, MRI, media, and forensics.
Categories: Computers

Sparse Image and Signal Processing

Sparse Image and Signal Processing

This thoroughly updated new edition presents state-of-the-art sparse and multiscale image and signal processing.

Author: Jean-Luc Starck

Publisher: Cambridge University Press

ISBN: 9781316483305

Category: Computers

Page:

View: 364

This thoroughly updated new edition presents state-of-the-art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB® and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications.
Categories: Computers

Sparse and Redundant Representations

Sparse and Redundant Representations

This book provides a comprehensive view of the topic of sparse and redundant representation modeling, and its use in signal and image processing.

Author: Michael Elad

Publisher: Springer Science & Business Media

ISBN: 9781441970114

Category: Mathematics

Page: 376

View: 842

A long long time ago, echoing philosophical and aesthetic principles that existed since antiquity, William of Ockham enounced the principle of parsimony, better known today as Ockham’s razor: “Entities should not be multiplied without neces sity. ” This principle enabled scientists to select the ”best” physical laws and theories to explain the workings of the Universe and continued to guide scienti?c research, leadingtobeautifulresultsliketheminimaldescriptionlength approachtostatistical inference and the related Kolmogorov complexity approach to pattern recognition. However, notions of complexity and description length are subjective concepts anddependonthelanguage“spoken”whenpresentingideasandresults. The?eldof sparse representations, that recently underwent a Big Bang like expansion, explic itly deals with the Yin Yang interplay between the parsimony of descriptions and the “language” or “dictionary” used in them, and it became an extremely exciting area of investigation. It already yielded a rich crop of mathematically pleasing, deep and beautiful results that quickly translated into a wealth of practical engineering applications. You are holding in your hands the ?rst guide book to Sparseland, and I am sure you’ll ?nd in it both familiar and new landscapes to see and admire, as well as ex cellent pointers that will help you ?nd further valuable treasures. Enjoy the journey to Sparseland! Haifa, Israel, December 2009 Alfred M. Bruckstein vii Preface This book was originally written to serve as the material for an advanced one semester (fourteen 2 hour lectures) graduate course for engineering students at the Technion, Israel.
Categories: Mathematics

Sparse Image and Signal Processing

Sparse Image and Signal Processing

This thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing.

Author: Jean-Luc Starck

Publisher:

ISBN: 1316104516

Category: Image processing

Page:

View: 622

This thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. --
Categories: Image processing

Sparse representation of visual data for compression and compressed sensing

Sparse representation of visual data for compression and compressed sensing

The theoretical results discussed here are useful for designing efficient capturing devices for multidimensional visual data.

Author: Ehsan Miandji

Publisher: Linköping University Electronic Press

ISBN: 9789176851869

Category:

Page: 158

View: 247

The ongoing advances in computational photography have introduced a range of new imaging techniques for capturing multidimensional visual data such as light fields, BRDFs, BTFs, and more. A key challenge inherent to such imaging techniques is the large amount of high dimensional visual data that is produced, often requiring GBs, or even TBs, of storage. Moreover, the utilization of these datasets in real time applications poses many difficulties due to the large memory footprint. Furthermore, the acquisition of large-scale visual data is very challenging and expensive in most cases. This thesis makes several contributions with regards to acquisition, compression, and real time rendering of high dimensional visual data in computer graphics and imaging applications. Contributions of this thesis reside on the strong foundation of sparse representations. Numerous applications are presented that utilize sparse representations for compression and compressed sensing of visual data. Specifically, we present a single sensor light field camera design, a compressive rendering method, a real time precomputed photorealistic rendering technique, light field (video) compression and real time rendering, compressive BRDF capture, and more. Another key contribution of this thesis is a general framework for compression and compressed sensing of visual data, regardless of the dimensionality. As a result, any type of discrete visual data with arbitrary dimensionality can be captured, compressed, and rendered in real time. This thesis makes two theoretical contributions. In particular, uniqueness conditions for recovering a sparse signal under an ensemble of multidimensional dictionaries is presented. The theoretical results discussed here are useful for designing efficient capturing devices for multidimensional visual data. Moreover, we derive the probability of successful recovery of a noisy sparse signal using OMP, one of the most widely used algorithms for solving compressed sensing problems.
Categories:

A Wavelet Tour of Signal Processing

A Wavelet Tour of Signal Processing

Features: * Balances presentation of the mathematics with applications to signal processing * Algorithms and numerical examples are implemented in WaveLab, a MATLAB toolbox New in this edition * Sparse signal representations in dictionaries ...

Author: Stephane Mallat

Publisher: Academic Press

ISBN: 0080922023

Category: Technology & Engineering

Page: 832

View: 751

Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth. - Laurent Demanet, Stanford University The new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today's signal processing. The book clearly presents the standard representations with Fourier, wavelet and time-frequency transforms, and the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse representations in redundant dictionaries, super-resolution and compressive sensing applications. Features: * Balances presentation of the mathematics with applications to signal processing * Algorithms and numerical examples are implemented in WaveLab, a MATLAB toolbox New in this edition * Sparse signal representations in dictionaries * Compressive sensing, super-resolution and source separation * Geometric image processing with curvelets and bandlets * Wavelets for computer graphics with lifting on surfaces * Time-frequency audio processing and denoising * Image compression with JPEG-2000 * New and updated exercises A Wavelet Tour of Signal Processing: The Sparse Way, Third Edition, is an invaluable resource for researchers and R&D engineers wishing to apply the theory in fields such as image processing, video processing and compression, bio-sensing, medical imaging, machine vision and communications engineering. Stephane Mallat is Professor in Applied Mathematics at École Polytechnique, Paris, France. From 1986 to 1996 he was a Professor at the Courant Institute of Mathematical Sciences at New York University, and between 2001 and 2007, he co-founded and became CEO of an image processing semiconductor company. Includes all the latest developments since the book was published in 1999, including its application to JPEG 2000 and MPEG-4 Algorithms and numerical examples are implemented in Wavelab, a MATLAB toolbox Balances presentation of the mathematics with applications to signal processing
Categories: Technology & Engineering

Image and Signal Processing for Remote Sensing

Image and Signal Processing for Remote Sensing

Determining a sparse set of point matches among the images is a key problem
for multi - view analysis . It is usually performed as a first step in order to calibrate
( fully or weakly ) the system , when anything about the geometry of the imaging ...

Author:

Publisher:

ISBN: UOM:39015064356424

Category: Image processing

Page:

View: 121

Categories: Image processing

Sparse Signal Processing Based Image Compression and Inpainting

Sparse Signal Processing Based Image Compression and Inpainting

In this thesis, we investigate the application of compressive sensing and sparse signal processing techniques to image compression and inpainting problems.

Author: Rashwan M. Almshaal

Publisher:

ISBN: OCLC:951679082

Category:

Page:

View: 333

In this thesis, we investigate the application of compressive sensing and sparse signal processing techniques to image compression and inpainting problems. Considering that many signals are sparse in certain transformation domain, a natural question to ask is: can an image be represented by as few coefficients as possible? In this thesis, we propose a new model for image compression/decompression based on sparse representation. We suggest constructing an overcomplete dictionary by combining two compression matrices, the discrete cosine transform (DCT) matrix and Hadamard-Walsh transform (HWT) matrix, instead of using only one transformation matrix that has been used by the common compression techniques such as JPEG and JPEG2000. We analyze the Structural Similarity Index (SSIM) versus the number of coefficients, measured by the Normalized Sparse Coefficient Rate (NSCR) for our approach. We observe that using the same NSCR, SSIM for images compressed using the proposed approach is between 4%-17% higher than when using JPEG. Several algorithms have been used for sparse coding. Based on experimental results, Orthogonal Matching Pursuit (OMP) is proved to be the most efficient algorithm in terms of computational time and the quality of the decompressed image. In addition, based on compressive sensing techniques, we propose an image inpainting approach, which could be used to fill missing pixels and reconstruct damaged images. In this approach, we use the Gradient Projection for Sparse Reconstruction (GPSR) algorithm and wavelet transformation with Daubechies filters to reconstruct the damaged images based on the information available in the original image. Experimental results show that our approach outperforms existing image inpainting techniques in terms of computational time with reasonably good image reconstruction performance.
Categories:

Hybrid Image and Signal Processing

Hybrid Image and Signal Processing

The nonzero entries of the sparse matrices Q ( k , 2 ) are given by ( cf. ( 4 ) ) : ( k ,
1 ) eine 92,2 91 , j ( k , 1 ) ( k , 1 ) ( k , 1 ) eno + 1 , no + 1 + i - j ) 1 < i < i < no , ( k ,
1 ) ( k , 1 ) - eno + 1 - i + j , ng + ? ' 1 < i < i < no , eno + 1 + i , no + 1 + ; eno + 1 ...

Author:

Publisher:

ISBN: UOM:39015048213212

Category: Image processing

Page:

View: 405

Categories: Image processing

Hybrid Image and Signal Processing

Hybrid Image and Signal Processing

The excellent speedup of algorithms on sparse images follows from noticing that
for sparse images the running times are dominated by the internal processing
time to find the possible extreme points , which is O ( M / N ) . So as the number of
 ...

Author: David Paul Casasent

Publisher: Society of Photo Optical

ISBN: UCSD:31822003191129

Category: Electronic book

Page: 259

View: 271

Categories: Electronic book

Signal and Image Processing for Remote Sensing Second Edition

Signal and Image Processing for Remote Sensing  Second Edition

This book combines both, providing a unique balance between the role of signal processing and image processing. Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches.

Author: C.H. Chen

Publisher: CRC Press

ISBN: 9781439855966

Category: Technology & Engineering

Page: 619

View: 905

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting information from remotely sensed waveforms or time series data. This book combines both, providing a unique balance between the role of signal processing and image processing. Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches. Not limited to satellite data, it also considers signals and images from hydroacoustic, seismic, microwave, and other sensors. Chapters cover important topics in signal and image processing and discuss techniques for dealing with remote sensing problems. Each chapter offers an introduction to the topic before delving into research results, making the book accessible to a broad audience. This second edition reflects the considerable advances that have occurred in the field, with 23 of 27 chapters being new or entirely rewritten. Coverage includes new mathematical developments such as compressive sensing, empirical mode decomposition, and sparse representation, as well as new component analysis methods such as non-negative matrix and tensor factorization. The book also presents new experimental results on SAR and hyperspectral image processing. The emphasis is on mathematical techniques that will far outlast the rapidly changing sensor, software, and hardware technologies. Written for industrial and academic researchers and graduate students alike, this book helps readers connect the "dots" in image and signal processing. New in This Edition The second edition includes four chapters from the first edition, plus 23 new or entirely rewritten chapters, and 190 new figures. New topics covered include: Compressive sensing The mixed pixel problem with hyperspectral images Hyperspectral image (HSI) target detection and classification based on sparse representation An ISAR technique for refocusing moving targets in SAR images Empirical mode decomposition for signal processing Feature extraction for classification of remote sensing signals and images Active learning methods in classification of remote sensing images Signal subspace identification of hyperspectral data Wavelet-based multi/hyperspectral image restoration and fusion The second edition is not intended to replace the first edition entirely and readers are encouraged to read both editions of the book for a more complete picture of signal and image processing in remote sensing. See Signal and Image Processing for Remote Sensing (CRC Press 2006).
Categories: Technology & Engineering

Intelligent Signal Processing

Intelligent Signal Processing

Sparse coding can now be formulated as a search ing . We model the noise - free
image data by independent component for a weight matrix W such that the
components s , are as analysis and denoise a noisy image by maximum
likelihood ...

Author: Simon Haykin

Publisher: Wiley-IEEE Press

ISBN: UOM:39015050503427

Category: Technology & Engineering

Page: 573

View: 462

"IEEE Press is proud to present the first selected reprint volume devoted to the new field of intelligent signal processing (ISP). ISP differs fundamentally from the classical approach to statistical signal processing in that the input-output behavior of a complex system is modeled by using "intelligent" or "model-free" techniques, rather than relying on the shortcomings of a mathematical model. Information is extracted from incoming signal and noise data, making few assumptions about the statistical structure of signals and their environment. Intelligent Signal Processing explores how ISP tools address the problems of practical neural systems, new signal data, and blind fuzzy approximators. The editors have compiled 20 articles written by prominent researchers covering 15 diverse, practical applications of this nascent topic, exposing the reader to the signal processing power of learning and adaptive systems. This essential reference is intended for researchers, professional engineers, and scientists working in statistical signal processing and its applications in various fields such as humanistic intelligence, stochastic resonance, financial markets, optimization, pattern recognition, signal detection, speech processing, and sensor fusion. Intelligent Signal Processing is also invaluable for graduate students and academics with a background in computer science, computer engineering, or electrical engineering. About the Editors Simon Haykin is the founding director of the Communications Research Laboratory at McMaster University, Hamilton, Ontario, Canada, where he serves as university professor. His research interests include nonlinear dynamics, neural networks and adaptive filters and their applications in radar and communications systems. Dr. Haykin is the editor for a series of books on "Adaptive and Learning Systems for Signal Processing, Communications and Control" (Publisher) and is both an IEEE Fellow and Fellow of the Royal Society of Canada. Bart Kosko is a past director of the University of Southern California's (USC) Signal and Image Processing Institute. He has authored several books, including Neural Networks and Fuzzy Systems, Neural Networks for Signal Processing (Publisher, copyright date) and Fuzzy Thinking (Publisher, copyright date), as well as the novel Nanotime (Publisher, copyright date). Dr. Kosko is an elected governor of the International Neural Network Society and has chaired many neural and fuzzy system conferences. Currently, he is associate professor of electrical engineering at USC."
Categories: Technology & Engineering

Statistical methods for image and signal processing

Statistical methods for image and signal processing

2.5 Discussion We have shown that a wavelet basis and a mixture prior
composed of a Dirac delta function and a Gaussian can be adapted to natural
images resulting in very sparse image representations . The resulting learned
basis is ...

Author: Philip Andrew Sallee

Publisher:

ISBN: UCAL:X68608

Category:

Page: 262

View: 861

Categories: