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.

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.

Blind deconvolution is a classical image processing problem which has been investigated by a large number of researchers over the last four decades. The purpose of this monograph is not to propose yet another method for blind image restoration. Rather the basic issue of deconvolvability has been explored from a theoretical view point. Some authors claim very good results while quite a few claim that blind restoration does not work. The authors clearly detail when such methods are expected to work and when they will not. In order to avoid the assumptions needed for convergence analysis in the Fourier domain, the authors use a general method of convergence analysis used for alternate minimization based on three point and four point properties of the points in the image space. The authors prove that all points in the image space satisfy the three point property and also derive the conditions under which four point property is satisfied. This provides the conditions under which alternate minimization for blind deconvolution converges with a quadratic prior. Since the convergence properties depend on the chosen priors, one should design priors that avoid trivial solutions. Hence, a sparsity based solution is also provided for blind deconvolution, by using image priors having a cost that increases with the amount of blur, which is another way to prevent trivial solutions in joint estimation. This book will be a highly useful resource to the researchers and academicians in the specific area of blind deconvolution.

Comprehensive guide to the restoration of images degraded by motion blur, encompassing algorithms and architectures, with novel computational photography methods.

It is well known that Intersymbol (ISI) Interference is a limiting factor in many communication environments where it causes an irreducible degradation of the bit error rate (BER) thus imposing an upper limit on the data symbol rate. In order to overcome the ISI problem, an equalizer is implemented in those systems. Among the three types of equalizers - non-blind, semi-blind and blind - the blind equalizer has the benefit of bandwidth saving and there is no need of going through a training phase. Blind equalization algorithms are essentially adaptive filtering algorithms designed such that they do not require the external supply of a desired response to generate the error signal in the output of the adaptive equalization filter. the algorithms generate an estimate of the desired response by applying a nonlinear transformation to sequences involved in the adaptation process. This nonlinearity is designed to minimize a cost function that is implicitly based on higher order statistics (HOS) according to one approach, or calculated directly according to the Bayes rules. The Whole Story behind Blind Adaptive Equalizers/ Blind Deconvolution gives the readers a full understanding on the blind deconvolution. the e-book covers a variety of blind deconvolution/equalization methods based on both cost functions and Bayes rules where simulation results are supplied to support the theory. These include the Maximum Entropy density approximation technique and the Edgeworth Expansion approach used in various blind equalizers. It also describes the relationship between the cost function approach and the approach taken according to Bayes rules. the e-book deals also with the effect of various system parameters (such as the step-size parameter or the equalizer's tap length) have on the obtained equalization performance. This e-book will be of particular interest to advanced communications engineering undergraduate students, graduate students, university instructors and signal processing researchers.

Many scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data. Unfortunately, most inverse problems are ill-posed, which means that precise and stable solutions are not easy to devise. Regularization is the key concept to solve inverse problems. The goal of this book is to deal with inverse problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimation. The first three chapters bring the theoretical notions that make it possible to cast inverse problems within a mathematical framework. The next three chapters address the fundamental inverse problem of deconvolution in a comprehensive manner. Chapters 7 and 8 deal with advanced statistical questions linked to image estimation. In the last five chapters, the main tools introduced in the previous chapters are put into a practical context in important applicative areas, such as astronomy or medical imaging.