Author: Michael WangPublisher:
ISBN:
Category :
Languages : en
Pages : 77
Book Description
Variation of a Multiresolutional Approach to Maximum Likelihood Blind Deconvolution
Author: Michael WangSelf-correcting Multi-channel Bussgang Blind Deconvolution Using Expectation Maximization (EM) Algorithm and Feedback
Author: Sze Ho TangPublisher:
ISBN:
Category : Mathematical optimization
Languages : en
Pages :
Book Description
A Bussgang based blind deconvolution algorithm called self-correcting multi-channel Bussgang (SCMB) blind deconvolution algorithm was proposed. Unlike the original Bussgang blind deconvolution algorithm where the probability density function (pdf) of the signal being recovered is assumed to be completely known, the proposed SCMB blind deconvolution algorithm relaxes this restriction by parameterized the pdf with a Gaussian mixture model and expectation maximization (EM) algorithm, an iterative maximum likelihood approach, is employed to estimate the parameter side by side with the estimation of the equalization filters of the original Bussgang blind deconvolution algorithm. A feedback loop is also designed to compensate the effect of the parameter estimation error on the estimation of the equalization filters. Application of the SCMB blind deconvolution framework for binary image restoration, multi-pass synthetic aperture radar (SAR) autofocus and inverse synthetic aperture radar (ISAR) autofocus are exploited with great results.
Blind Image Deconvolution
Author: Patrizio CampisiPublisher: CRC Press
ISBN: 1420007297
Category : Technology & Engineering
Languages : en
Pages : 474
Book Description
Blind image deconvolution is constantly receiving increasing attention from the academic as well the industrial world due to both its theoretical and practical implications. The field of blind image deconvolution has several applications in different areas such as image restoration, microscopy, medical imaging, biological imaging, remote sensing, astronomy, nondestructive testing, geophysical prospecting, and many others. Blind Image Deconvolution: Theory and Applications surveys the current state of research and practice as presented by the most recognized experts in the field, thus filling a gap in the available literature on blind image deconvolution. Explore the gamut of blind image deconvolution approaches and algorithms that currently exist and follow the current research trends into the future. This comprehensive treatise discusses Bayesian techniques, single- and multi-channel methods, adaptive and multi-frame techniques, and a host of applications to multimedia processing, astronomy, remote sensing imagery, and medical and biological imaging at the whole-body, small-part, and cellular levels. Everything you need to step into this dynamic field is at your fingertips in this unique, self-contained masterwork. For image enhancement and restoration without a priori information, turn to Blind Image Deconvolution: Theory and Applications for the knowledge and techniques you need to tackle real-world problems.
Quasi Maximum Likelihood Blind Deconvolution of Images Using Optimal Sparse Representations
Author: Alexander BronsteinBlind Deconvolution
Author: Simon S. HaykinPublisher: Prentice Hall
ISBN:
Category : Computers
Languages : en
Pages : 312
Book Description
This book is devoted to the study of the blind deconvolution problem - where it is impractical to assume the availability of the system input. It considers a variety of blind deconvolution/equalization algorithms - with computer simulation experiments to support the theory.
Parallel Implementation of the Multi-frame Blind Deconvolution Algorithm
Author: Jason MillerTotal Variation Blind Deconvolution
Author: Tony F. ChanInformation Processing in Medical Imaging
Author: C.N. De GraaffPublisher: Springer Science & Business Media
ISBN: 1461572630
Category : Computers
Languages : en
Pages : 612
Book Description
This book summarizes the proceedings of the 10th international conference on Infonnation Pro cessing in Medical Imaging (IPMI-lO), held in June, 1987, in Zeist, The Netherlands. IPMI is a biennial conference, organized alternately in Europe and North America. The subject of the conference is the use of physics, mathematics, computer science, and engineering in the of medical images. The intent of the conference is to fonnation, processing and interpretation provide a forum where new ideas and results of research in medical imaging can be presented and amply discussed. Accordingly, the programme can comprise only a limited number of papers. The scientific committee of IPMI-lO selected 41 papers for presentation, although a total of 102 extended abstracts of on the average high quality had been submitted. All selected contri butions are included in these proceedings. During of the preparations of the conference the organizers received the tragic news of the death of Francois Erbsmann, the initiator of IPMI, and organizer of the first conference in 1969 in Brussels. Francois always emphasized that the backbone of the IPMI meetings should be promising young and active researchers rather than established scientists in the field. As an appreciation of this idea, and in thankful remembrance of Francois' stimulating work, the IPMI-board has taken the initiative to present the Francois Erbsmann prize for the most significant contribution to the conference by a young investigator.
Science Abstracts
Author: Publisher:
ISBN:
Category : Electrical engineering
Languages : en
Pages : 1360
Book Description
Sparse Image and Signal Processing
Author: Jean-Luc StarckPublisher: Cambridge University Press
ISBN: 1316483304
Category : Computers
Languages : en
Pages : 449
Book Description
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.