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Inverse Problems

regularization, semismooth Newton method, direct sampling method, uncertainty quantification and approximate Bayesian inference. It is written for;

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Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing

into account. Clarifying and taking these pieces of information into account is necessary for grasping the domain of validity and the field;

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Numerical Methods for Inverse Problems

regularization; finite differences; and orthogonal decomposition; as well as those based on Bayesian inference, artificial neural networks, and quantum;

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Sparse Image and Signal Processing

regularization, sparse signal decomposition, blind source separation, and compressed sensing. This book weds theory and practice in examining;

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Computational Methods for Applied Inverse Problems

-statistical methods, e.g., regularization and optimization techniques for solving practical inverse problems.;

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Bayesian Approach to Inverse Problems

problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimation. The first three;

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Maximum Entropy and Bayesian Methods

This volume contains the proceedings of the Fifteenth International Workshop on Maximum Entropy and Bayesian Methods, held in Sante Fe, New;

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An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems

sources of error introduced by approximate mathematical models, regularization methods, numerical approximations for efficient computations, noisy;

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Mathematical Methods in Image Processing and Inverse Problems

applications were presented. The collected papers in this book also give new research trends in deep learning and optimization for imaging science. It;

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Optimal Methods for Ill-Posed Problems

The book covers fundamentals of the theory of optimal methods for solving ill-posed problems, as well as ways to obtain accurate and;

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Optimisation in Signal and Image Processing

This book describes the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian;

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Regularization, Optimization, Kernels, and Support Vector Machines

learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex;

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Regularization, Optimization, Kernels, and Support Vector Machines

learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex;

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Nonlinear Signal and Image Processing

Nonlinear signal and image processing methods are fast emerging as an alternative to established linear methods for meeting the challenges;

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Advances in Heuristic Signal Processing and Applications

There have been significant developments in the design and application of algorithms for both one-dimensional signal processing and;

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Regularization Algorithms for Ill-Posed Problems

operator equations, both linear and nonlinear. These approximation schemes form a basis for implementable numerical algorithms for the stable;

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Advances in Computational Mathematics

, geometric modelling, and signal and image processing and their applications.;

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Signal and Image Processing for Remote Sensing

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the;

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Variational Regularization for Systems of Inverse Problems

an approach allows to choose the regularization weights of each subproblem individually with respect to the corresponding noise levels and;

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Linear Inverse Problems and Tikhonov Regularization

of noise in data, and uses much of the machinery of Hilbert space theory. This book develops the theory of Tikhonov regularization for a certain;

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Practical Signal Processing

of digital signal processing, with emphasis on real-world applications. Sampling, quantization, the Fourier transform, filters, Bayesian methods and;

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Practical Signal Processing

of digital signal processing, with emphasis on real-world applications. Sampling, quantization, the Fourier transform, filters, Bayesian methods and;

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Signal and Image Processing for Remote Sensing

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the;

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Conjugate Gradient Type Methods for Ill-Posed Problems

) to ill posed problems and their regularization. Such problems occur in applications from almost all natural and technical sciences, including;

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Conjugate Gradient Type Methods for Ill-Posed Problems

) to ill posed problems and their regularization. Such problems occur in applications from almost all natural and technical sciences, including;

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Discrete Signals and Inverse Problems

examples and insightful exercises. Discrete Signals and Inverse Problems is essential reading for experimental researchers and practicing;

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Computational Methods for Electromagnetic Inverse Scattering

A comprehensive and updated overview of the theory, algorithms and applications of for electromagnetic inverse scattering problems;

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