Compressive Imaging Structure Sampling Learning
DOWNLOAD
Download Compressive Imaging Structure Sampling Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Compressive Imaging Structure Sampling Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Compressive Imaging Structure Sampling Learning
DOWNLOAD
Author : Ben Adcock
language : en
Publisher: Cambridge University Press
Release Date : 2021-09-16
Compressive Imaging Structure Sampling Learning written by Ben Adcock and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-16 with Computers categories.
Accurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging – including compressed sensing, wavelets and optimization – in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the pitfalls of these latest approaches.
High Dimensional Optimization And Probability
DOWNLOAD
Author : Ashkan Nikeghbali
language : en
Publisher: Springer Nature
Release Date : 2022-08-04
High Dimensional Optimization And Probability written by Ashkan Nikeghbali and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-04 with Mathematics categories.
This volume presents extensive research devoted to a broad spectrum of mathematics with emphasis on interdisciplinary aspects of Optimization and Probability. Chapters also emphasize applications to Data Science, a timely field with a high impact in our modern society. The discussion presents modern, state-of-the-art, research results and advances in areas including non-convex optimization, decentralized distributed convex optimization, topics on surrogate-based reduced dimension global optimization in process systems engineering, the projection of a point onto a convex set, optimal sampling for learning sparse approximations in high dimensions, the split feasibility problem, higher order embeddings, codifferentials and quasidifferentials of the expectation of nonsmooth random integrands, adjoint circuit chains associated with a random walk, analysis of the trade-off between sample size and precision in truncated ordinary least squares, spatial deep learning, efficient location-based tracking for IoT devices using compressive sensing and machine learning techniques, and nonsmooth mathematical programs with vanishing constraints in Banach spaces. The book is a valuable source for graduate students as well as researchers working on Optimization, Probability and their various interconnections with a variety of other areas. Chapter 12 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Numerical Analysis Meets Machine Learning
DOWNLOAD
Author :
language : en
Publisher: Elsevier
Release Date : 2024-06-13
Numerical Analysis Meets Machine Learning written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-13 with Mathematics categories.
Numerical Analysis Meets Machine Learning series, highlights new advances in the field, with this new volume presenting interesting chapters. Each chapter is written by an international board of authors. - Provides the authority and expertise of leading contributors from an international board of authors - Presents the latest release in the Handbook of Numerical Analysis series - Updated release includes the latest information on the Numerical Analysis Meets Machine Learning
Sparse Polynomial Approximation Of High Dimensional Functions
DOWNLOAD
Author : Ben Adcock
language : en
Publisher: SIAM
Release Date : 2022-02-16
Sparse Polynomial Approximation Of High Dimensional Functions written by Ben Adcock and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-16 with Mathematics categories.
Over seventy years ago, Richard Bellman coined the term “the curse of dimensionality” to describe phenomena and computational challenges that arise in high dimensions. These challenges, in tandem with the ubiquity of high-dimensional functions in real-world applications, have led to a lengthy, focused research effort on high-dimensional approximation—that is, the development of methods for approximating functions of many variables accurately and efficiently from data. This book provides an in-depth treatment of one of the latest installments in this long and ongoing story: sparse polynomial approximation methods. These methods have emerged as useful tools for various high-dimensional approximation tasks arising in a range of applications in computational science and engineering. It begins with a comprehensive overview of best s-term polynomial approximation theory for holomorphic, high-dimensional functions, as well as a detailed survey of applications to parametric differential equations. It then describes methods for computing sparse polynomial approximations, focusing on least squares and compressed sensing techniques. Sparse Polynomial Approximation of High-Dimensional Functions presents the first comprehensive and unified treatment of polynomial approximation techniques that can mitigate the curse of dimensionality in high-dimensional approximation, including least squares and compressed sensing. It develops main concepts in a mathematically rigorous manner, with full proofs given wherever possible, and it contains many numerical examples, each accompanied by downloadable code. The authors provide an extensive bibliography of over 350 relevant references, with an additional annotated bibliography available on the book’s companion website (www.sparse-hd-book.com). This text is aimed at graduate students, postdoctoral fellows, and researchers in mathematics, computer science, and engineering who are interested in high-dimensional polynomial approximation techniques.
A Mathematical Introduction To Compressive Sensing
DOWNLOAD
Author : Simon Foucart
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-08-13
A Mathematical Introduction To Compressive Sensing written by Simon Foucart and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-13 with Computers categories.
At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domains. In the areas of applied mathematics, electrical engineering, and theoretical computer science, an explosion of research activity has already followed the theoretical results that highlighted the efficiency of the basic principles. The elegant ideas behind these principles are also of independent interest to pure mathematicians. A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build. With only moderate prerequisites, it is an excellent textbook for graduate courses in mathematics, engineering, and computer science. It also serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject. A Mathematical Introduction to Compressive Sensing uses a mathematical perspective to present the core of the theory underlying compressive sensing.
Computer Vision Eccv 2024
DOWNLOAD
Author : Aleš Leonardis
language : en
Publisher: Springer Nature
Release Date : 2024-10-24
Computer Vision Eccv 2024 written by Aleš Leonardis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-24 with Computers categories.
The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024. The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation.
Principles Of Electron Optics Volume 4
DOWNLOAD
Author : Peter W. Hawkes
language : en
Publisher: Academic Press
Release Date : 2022-05-10
Principles Of Electron Optics Volume 4 written by Peter W. Hawkes and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-10 with Technology & Engineering categories.
Principles of Electron Optics: Second Edition, Advanced Wave Optics provides a self-contained, modern account of electron optical phenomena with the Dirac or Schrödinger equation as a starting point. Knowledge of this branch of the subject is essential to understanding electron propagation in electron microscopes, electron holography and coherence. Sections in this new release include, Electron Interactions in Thin Specimens, Digital Image Processing, Acquisition, Sampling and Coding, Enhancement, Linear Restoration, Nonlinear Restoration – the Phase Problem, Three-dimensional Reconstruction, Image Analysis, Instrument Control, Vortex Beams, The Quantum Electron Microscope, and much more. - Includes authoritative coverage of many recent developments in wave electron optics - Describes the interaction of electrons with solids and the information that can be obtained from electron-beam techniques - Includes new content on multislice optics, 3D reconstruction, Wigner optics, vortex beams and the quantum electron microscope
High Throughput Imaging Technology
DOWNLOAD
Author : Zhengjun Liu
language : en
Publisher: Springer Nature
Release Date : 2025-03-07
High Throughput Imaging Technology written by Zhengjun Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-07 with Science categories.
This book highlights a comprehensive introduction to high-throughput imaging, with the focus on the principles and methods. High-throughput imaging has become a research trend in the field of optics. It combines fast imaging, super-resolution imaging and large field of view imaging, improving the performance of the imaging system in many aspects. The development of a fast and high-throughput imaging system requires integration of optics, mathematics, programming, and other related science and technology. They bridge the theory and the system and realize the software-hardware integration, finally achieving high-performance imaging. An effective evaluation criterion of high-throughput imaging is the spatio-temporal bandwidth product, which provides guidance for research. The imaging technology with better comprehensive performance is the key target of research. Nowadays, new super-resolution imaging technologies and high-throughput imaging technologies have been emerging one after another, together with a number of new technical indicators. However, the integration and cascade of various technologies is still a very difficult challenge, and different technologies are difficult to be used in combination because of differences in physical space and technical means. Creating an imaging system with fast and high-throughput imaging capability is an urgent research task, which has important economic and social benefits for practical applications such as observing the dynamic (transient) process of large-size targets and on-line detection. High-throughput imaging is one of the major research goals of global research teams in optical imaging. This book summarizes latest research advances and introduces a variety of imaging methods targeting key problems, bringing together new theories and technologies in the aspects of high resolution, large field of view and fast imaging. The book provides a handy reference and systematic handbook for graduate students, researchers, and technicians engaged in the study, research and work in optical imaging.
Magnetic Resonance Image Reconstruction
DOWNLOAD
Author : Mehmet Akcakaya
language : en
Publisher: Academic Press
Release Date : 2022-11-04
Magnetic Resonance Image Reconstruction written by Mehmet Akcakaya and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-04 with Science categories.
Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. The book discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. This unique resource is suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI. - Explains the underlying principles of MRI reconstruction, along with the latest research - Gives example codes for some of the methods presented - Includes updates on the latest developments, including compressed sensing, tensor-based reconstruction and machine learning based reconstruction
Sparse Representation Of Visual Data For Compression And Compressed Sensing
DOWNLOAD
Author : Ehsan Miandji
language : en
Publisher: Linköping University Electronic Press
Release Date : 2018-11-23
Sparse Representation Of Visual Data For Compression And Compressed Sensing written by Ehsan Miandji and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-23 with categories.
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.