The Use of Compressive Sensing in Video
The Use of Compressive Sensing in Video

Author:

Publisher:

Published: 2013

Total Pages: 47

ISBN-13:

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Compressed Sensing (CS) is a recently developed tool for signal acquisition and compression in a single stage. In this thesis, we explore the nascent field of compressive sensing for images and video. The performance of different CS acquisition and reconstruction schemes for images are compared and analyzed. The quality of reconstruction corresponding to different measurement fractions is observed. Frame-by-frame mode and video block mode of CS for video are explored. Hybrid schemes that combine the ideas of CS and that of traditional video compression algorithms are studied. Specifically, two schemes, one having two layers of linear measurements at the encoder and two rounds of reconstruction at the decoder, and a second scheme using iterative reweighting along with morphological image processing at the decoder to reconstruct with better quality for lesser measurements are implemented.

Data-Driven Science and Engineering
Data-Driven Science and Engineering

Author: Steven L. Brunton

Publisher: Cambridge University Press

Published: 2022-05-05

Total Pages: 615

ISBN-13: 1009098489

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A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Handbook of Mathematical Methods in Imaging
Handbook of Mathematical Methods in Imaging

Author: Otmar Scherzer

Publisher: Springer Science & Business Media

Published: 2010-11-23

Total Pages: 1626

ISBN-13: 0387929193

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The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful.

Compressive Sensing for Wireless Networks
Compressive Sensing for Wireless Networks

Author: Zhu Han

Publisher: Cambridge University Press

Published: 2013-06-06

Total Pages: 308

ISBN-13: 1107018838

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This comprehensive reference delivers the understanding and skills needed to take advantage of compressive sensing in wireless networks.

Block-based Compressed Sensing of Images and Video
Block-based Compressed Sensing of Images and Video

Author: James E. Fowler

Publisher: Foundations and Trends(r) in S

Published: 2012

Total Pages: 134

ISBN-13: 9781601985200

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Block-Based Compressed Sensing of Images and Video overviews the emerging concept of compressed sensing (CS) with a particular focus on recent proposals for its use with a variety of imaging media, including still images, motion video, as well as multiview images and video. Throughout, it considers a variety of CS reconstruction techniques proposed in recent literature and examines relative performance of several prominent reconstruction algorithms for each of the various imagery formats. Particular emphasis is placed on block-based measurement and reconstruction which has the advantages of significantly reduced memory and computation with respect to other approaches relying on full-frame CS measurement operators. Block-Based Compressed Sensing of Images and Video employs extensive experimental comparisons to evaluate various prominent reconstruction algorithms for still-image, motion-video, and multiview scenarios in terms of both reconstruction quality as well as computational complexity. It is not intended to serve as an indepth tutorial on the theory or mathematics of compressed sensing. The coverage of CS theory is brief, while the specifics of the application of block-based compressed sensing (BCS) to natural imagery consume the bulk of the discussion.

Compressed Sensing and its Applications
Compressed Sensing and its Applications

Author: Holger Boche

Publisher: Birkhäuser

Published: 2015-07-04

Total Pages: 475

ISBN-13: 3319160427

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Since publication of the initial papers in 2006, compressed sensing has captured the imagination of the international signal processing community, and the mathematical foundations are nowadays quite well understood. Parallel to the progress in mathematics, the potential applications of compressed sensing have been explored by many international groups of, in particular, engineers and applied mathematicians, achieving very promising advances in various areas such as communication theory, imaging sciences, optics, radar technology, sensor networks, or tomography. Since many applications have reached a mature state, the research center MATHEON in Berlin focusing on "Mathematics for Key Technologies", invited leading researchers on applications of compressed sensing from mathematics, computer science, and engineering to the "MATHEON Workshop 2013: Compressed Sensing and its Applications” in December 2013. It was the first workshop specifically focusing on the applications of compressed sensing. This book features contributions by the plenary and invited speakers of this workshop. To make this book accessible for those unfamiliar with compressed sensing, the book will not only contain chapters on various applications of compressed sensing written by plenary and invited speakers, but will also provide a general introduction into compressed sensing. The book is aimed at both graduate students and researchers in the areas of applied mathematics, computer science, and engineering as well as other applied scientists interested in the potential and applications of the novel methodology of compressed sensing. For those readers who are not already familiar with compressed sensing, an introduction to the basics of this theory will be included.

Reconstruction-Free Compressive Vision for Surveillance Applications
Reconstruction-Free Compressive Vision for Surveillance Applications

Author: Henry Braun

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 86

ISBN-13: 3031025415

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Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision and deep learning have enabled new applications of automated systems. In this book, we introduce reconstruction-free compressive vision, where image processing and computer vision algorithms are embedded directly in the compressive domain, without the need for first reconstructing the measurements into images or video. Reconstruction of CS images is computationally expensive and adds to system complexity. Therefore, reconstruction-free compressive vision is an appealing alternative particularly for power-aware systems and bandwidth-limited applications that do not have on-board post-processing computational capabilities. Engineers must balance maintaining algorithm performance while minimizing both the number of measurements needed and the computational requirements of the algorithms. Our study explores the intersection of compressed sensing and computer vision, with the focus on applications in surveillance and autonomous navigation. Other applications are also discussed at the end and a comprehensive list of references including survey papers are given for further reading.

A Mathematical Introduction to Compressive Sensing
A Mathematical Introduction to Compressive Sensing

Author: Simon Foucart

Publisher: Springer Science & Business Media

Published: 2013-08-13

Total Pages: 634

ISBN-13: 0817649484

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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.