By Simon Foucart
At the intersection of arithmetic, engineering, and computing device technological know-how sits the thriving box of compressive sensing. in response to the idea that facts acquisition and compression could be played concurrently, compressive sensing unearths functions in imaging, sign processing, and lots of different domain names. within the components of utilized arithmetic, electric engineering, and theoretical computing device technological know-how, an explosion of study job has already the theoretical effects that highlighted the potency of the elemental rules. The based rules at the back of those rules also are of self sustaining curiosity to natural mathematicians.
A Mathematical creation to Compressive Sensing supplies an in depth account of the center conception upon which the sector is construct. With simply average necessities, it's a great textbook for graduate classes in arithmetic, engineering, and laptop technological know-how. It additionally serves as a competent source for practitioners and researchers in those disciplines who are looking to gather a cautious knowing of the topic. A Mathematical creation to Compressive Sensing makes use of a mathematical point of view to provide the middle of the speculation underlying compressive sensing.
Read Online or Download A Mathematical Introduction to Compressive Sensing PDF
Best imaging systems books
Copublished with JCD Publishing. Thorough clarification of warmth move, with recommendations supported by means of thermograms. meant for all who paintings with thermal imaging platforms: researchers, process designers, attempt engineers, revenues employees, and armed forces and civilian finish clients. Contents - advent - warmth - Blackbody radiation - Emissivity - Atmospheric transmittance - digicam layout - functionality parameters - digital camera choice - Observer education - advent to purposes - objective Signatures - Temperature measurements - development envelope inspections - Roof inspections - strength distribution - Electrical/Mechanical Inspection - Buried items - Surveillance - Nondestructive trying out - strategy/ qc - Inspection methods - Appendix A: Temperature conversion - Appendix B: Emissivity - Appendix C: Thermal Sensing and Imaging - Index
A finished remedy of assorted techniques to monochrome and electronic halftoning. It describes perfect spatial and spectral features of eco-friendly- and blue-noise halftoning that offer styles of appearance and computational complexity in quite a lot of printing applied sciences. The publication comes with a CD-ROM of algorithms and facts with color examples.
Indications and pictures: Advances and leads to Speech, Estimation, Compression, acceptance, Filtering, and Processing cohesively combines contributions from box specialists to bring a complete account of the most recent advancements in sign processing. those specialists element the result of their learn relating to audio and speech enhancement, acoustic snapshot estimation, video compression, biometric attractiveness, hyperspectral photograph research, tensor decomposition with purposes in communications, adaptive sparse-interpolated filtering, sign processing for energy line communications, bio-inspired sign processing, seismic information processing, mathematics transforms for spectrum computation, particle filtering in cooperative networks, 3-dimensional tv, and extra.
- Variational Methods in Imaging
- 2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
- Fiber-Optic Communication Systems, Fourth Edition
- IMAGING AND ELECTRON PHYSICS
- Principles of Digital Image Processing: Advanced Methods
- The digital signal processing fundamentals
Additional resources for A Mathematical Introduction to Compressive Sensing
9) in  and realized that methods of compressive sensing (the restricted isometry property) are useful for the analysis of sparse regression methods. We refer to  and the monograph  for details. For more information on machine learning, we direct the reader to [18, 133, 134, 444]. Connections between sparsity and machine learning can be found, for instance, in [23, 147, 513]. Low-rank matrix recovery. The extension of compressive sensing to the recovery of low-rank matrices from incomplete information emerged with the papers [90, 99, 418].
Although certainly interesting, we will not treat low-rank recovery extensively in this book. Nevertheless, due to the close analogy with sparse recovery, the main results are covered in exercises, and the reader is invited to work through them. 3 Overview of the Book Before studying the standard compressive sensing problem on a technical level, it is beneficial to draw a road map of the basic results and solving strategies presented in this book. As previously revealed, the notions of sparsity and compressibility are at the core of compressive sensing.
The theory currently available predicts that sampling sets K chosen uniformly at random among all possible sets of cardinality m work well (at least when W is the identity matrix). Indeed, the results of Chap. 12 guarantee that an s-sparse x ∈ CN can be reconstructed by 1 -minimization if m ≥ Cs ln N . Unfortunately, such random sets K are difficult to realize in practice due to the continuity constraints of the trajectories curves k1 , . . , kL . Therefore, good realizable sets K are investigated empirically.
A Mathematical Introduction to Compressive Sensing by Simon Foucart