Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Computational Imaging And Inverse Techniques For High Resolution And Instantaneous Spectral Imaging PDF full book. Access full book title Computational Imaging And Inverse Techniques For High Resolution And Instantaneous Spectral Imaging.
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 2014 |
| Genre | : |
| ISBN | : |
| Author | : Ayush Bhandari |
| Publisher | : MIT Press |
| Total Pages | : 482 |
| Release | : 2022-10-25 |
| Genre | : Technology & Engineering |
| ISBN | : 0262368374 |
A comprehensive and up-to-date textbook and reference for computational imaging, which combines vision, graphics, signal processing, and optics. Computational imaging involves the joint design of imaging hardware and computer algorithms to create novel imaging systems with unprecedented capabilities. In recent years such capabilities include cameras that operate at a trillion frames per second, microscopes that can see small viruses long thought to be optically irresolvable, and telescopes that capture images of black holes. This text offers a comprehensive and up-to-date introduction to this rapidly growing field, a convergence of vision, graphics, signal processing, and optics. It can be used as an instructional resource for computer imaging courses and as a reference for professionals. It covers the fundamentals of the field, current research and applications, and light transport techniques. The text first presents an imaging toolkit, including optics, image sensors, and illumination, and a computational toolkit, introducing modeling, mathematical tools, model-based inversion, data-driven inversion techniques, and hybrid inversion techniques. It then examines different modalities of light, focusing on the plenoptic function, which describes degrees of freedom of a light ray. Finally, the text outlines light transport techniques, describing imaging systems that obtain micron-scale 3D shape or optimize for noise-free imaging, optical computing, and non-line-of-sight imaging. Throughout, it discusses the use of computational imaging methods in a range of application areas, including smart phone photography, autonomous driving, and medical imaging. End-of-chapter exercises help put the material in context.
| Author | : Keith J. Dillon |
| Publisher | : |
| Total Pages | : 160 |
| Release | : 2014 |
| Genre | : |
| ISBN | : 9781303818103 |
With modern optimization techniques it is generally possible to form some kind of image estimate using an arbitrary collection of views or sensor measurements of an unknown object. The question is what is the best result that one can get with that data and will it be acceptable? We use optimization theory to analyze inverse problems such as this and formulate strategies for understanding how much high resolution information is contained in the data and how to intelligently reconstruct the best image possible. We start by formulating the reconstruction problem for a variety of different imaging systems, which we can view as special cases or extensions of tomography. In order to understand the range of potential solutions to such inverse problems, we formulate the optimization problems of bounding the elements of the unknown. We proceed to develop a novel approach to investigating inverse problems by defining uniqueness as the case where the upper and lower bounds on possible values each element can take are equal. This allows us both to express uniqueness on an element-wise basis, e.g. some elements may be uniquely-reconstructed while others may not, and to formulate conditions for uniqueness. These conditions provide a generalization of known conditions for unique solutions to systems with sparse or non-negative unknowns. We use these uniqueness conditions to pose a novel approach to estimation of system resolution and to thereby find low-resolution approximations which provide as much resolution as possible while retaining the uniqueness of the solution. Finally we demonstrate the extension to noisy and regularized problems using a Bayesian formulation which allows us to form trade-offs between resolution and uncertainty.
| Author | : Marco Donatelli |
| Publisher | : Springer Nature |
| Total Pages | : 171 |
| Release | : 2019-11-26 |
| Genre | : Mathematics |
| ISBN | : 3030328821 |
This book presents recent mathematical methods in the area of inverse problems in imaging with a particular focus on the computational aspects and applications. The formulation of inverse problems in imaging requires accurate mathematical modeling in order to preserve the significant features of the image. The book describes computational methods to efficiently address these problems based on new optimization algorithms for smooth and nonsmooth convex minimization, on the use of structured (numerical) linear algebra, and on multilevel techniques. It also discusses various current and challenging applications in fields such as astronomy, microscopy, and biomedical imaging. The book is intended for researchers and advanced graduate students interested in inverse problems and imaging.
| Author | : Zhengjun Liu |
| Publisher | : Springer Nature |
| Total Pages | : 420 |
| Release | : |
| Genre | : |
| ISBN | : 9819714559 |
| Author | : Stephen Joseph Olivas |
| Publisher | : |
| Total Pages | : 126 |
| Release | : 2015 |
| Genre | : |
| ISBN | : 9781321765274 |
There is an ongoing demand on behalf of the consumer, medical and military industries to make lighter weight, higher resolution, wider field-of-view and extended depth-of-focus cameras. This leads to design trade-offs between performance and cost, be it size, weight, power, or expense. This has brought attention to finding new ways to extend the design space while adhering to cost constraints. Extending the functionality of an imager in order to achieve extraordinary performance is a common theme of computational imaging, a field of study which uses additional hardware along with tailored algorithms to formulate and solve inverse problems in imaging. This dissertation details four specific systems within this emerging field: a Fiber Bundle Relayed Imaging System, an Extended Depth-of-Focus Imaging System, a Platform Motion Blur Image Restoration System, and a Compressive Imaging System. The Fiber Bundle Relayed Imaging System is part of a larger project, where the work presented in this thesis was to use image processing techniques to mitigate problems inherent to fiber bundle image relay and then, form high-resolution wide field-of-view panoramas captured from multiple sensors within a custom state-of-the-art imager. The Extended Depth-of-Focus System goals were to characterize the angular and depth dependence of the PSF of a focal swept imager in order to increase the acceptably focused imaged scene depth. The goal of the Platform Motion Blur Image Restoration System was to build a system that can capture a high signal-to-noise ratio (SNR), long-exposure image which is inherently blurred while at the same time capturing motion data using additional optical sensors in order to deblur the degraded images. Lastly, the objective of the Compressive Imager was to design and build a system functionally similar to the Single Pixel Camera and use it to test new sampling methods for image generation and to characterize it against a traditional camera. These computational imaging systems share a common theme in that they seek to accomplish camera designs that meet more demanding system requirements through the use of additional measurements made possible by hardware modifications, while relying on modeling and computational methods in order to provide valuable scene information.
| Author | : |
| Publisher | : |
| Total Pages | : 464 |
| Release | : 2006 |
| Genre | : Image processing |
| ISBN | : |
| Author | : Vishal Monga |
| Publisher | : Springer |
| Total Pages | : 238 |
| Release | : 2017-10-27 |
| Genre | : Computers |
| ISBN | : 3319616099 |
This book covers recent advances in image processing and imaging sciences from an optimization viewpoint, especially convex optimization with the goal of designing tractable algorithms. Throughout the handbook, the authors introduce topics on the most key aspects of image acquisition and processing that are based on the formulation and solution of novel optimization problems. The first part includes a review of the mathematical methods and foundations required, and covers topics in image quality optimization and assessment. The second part of the book discusses concepts in image formation and capture from color imaging to radar and multispectral imaging. The third part focuses on sparsity constrained optimization in image processing and vision and includes inverse problems such as image restoration and de-noising, image classification and recognition and learning-based problems pertinent to image understanding. Throughout, convex optimization techniques are shown to be a critically important mathematical tool for imaging science problems and applied extensively. Convex Optimization Methods in Imaging Science is the first book of its kind and will appeal to undergraduate and graduate students, industrial researchers and engineers and those generally interested in computational aspects of modern, real-world imaging and image processing problems.
| Author | : Charles A. Bouman |
| Publisher | : SIAM |
| Total Pages | : 350 |
| Release | : 2022-07-06 |
| Genre | : Mathematics |
| ISBN | : 1611977134 |
Collecting a set of classical and emerging methods previously unavailable in a single resource, Foundations of Computational Imaging: A Model-Based Approach is the first book to define a common foundation for the mathematical and statistical methods used in computational imaging. The book brings together a blend of research with applications in a variety of disciplines, including applied math, physics, chemistry, optics, and signal processing, to address a collection of problems that can benefit from a common set of methods. Readers will find basic techniques of model-based image processing, a comprehensive treatment of Bayesian and regularized image reconstruction methods, and an integrated treatment of advanced reconstruction techniques, such as majorization, constrained optimization, alternating direction method of multipliers (ADMM), and Plug-and-Play methods for model integration. Foundations of Computational Imaging can be used in courses on model-based or computational imaging, advanced numerical analysis, data science, numerical optimization, and approximation theory. It will also prove useful to researchers or practitioners in medical, scientific, commercial, and industrial imaging.
| Author | : Dr. Yuetong Lin |
| Publisher | : DEStech Publications, Inc |
| Total Pages | : 460 |
| Release | : 2014-11-16 |
| Genre | : Computers |
| ISBN | : 1605951323 |
The main objective of ICCSAI2013 is to provide a platform for the presentation of top and latest research results in global scientific areas. The conference aims to provide a high level international forum for researcher, engineers and practitioners to present and discuss recent advances and new techniques in computer science and artificial intelligence. It also serves to foster communications among researcher, engineers and practitioners working in a common interest in improving computer science, artificial intelligence and the related fields. We have received 325 numbers of papers through "Call for Paper", out of which 94 numbers of papers were accepted for publication in the conference proceedings through double blind review process. The conference is designed to stimulate the young minds including Research Scholars, Academicians, and Practitioners to contribute their ideas, thoughts and nobility in these two disciplines.
