[PDF] Optimal Co Design Of Computational Imaging Systems eBook

Author : Amit Ashok
Publisher :
Page : 356 pages
File Size : 32,93 MB
Release : 2008
Category :
ISBN :

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The traditional approach to imaging system design places the sole burden of image formation on optical components. In contrast, a computational imaging system relies on a combination of optics and post-processing to produce the final image and/or output measurement. Therefore, the joint-optimization (JO) of the optical and the post-processing degrees of freedom plays a critical role in the design of computational imaging systems. The JO framework also allows us to incorporate task-specific performance measures to optimize an imaging system for a specific task. In this dissertation, we consider the design of computational imaging systems within a JO framework for two separate tasks: object reconstruction and iris-recognition. The goal of these design studies is to optimize the imaging system to overcome the performance degradations introduced by under-sampled image measurements. Within the JO framework, we engineer the optical point spread function (PSF) of the imager, representing the optical degrees of freedom, in conjunction with the post-processing algorithm parameters to maximize the task performance. For the object reconstruction task, the optimized imaging system achieves a 50% improvement in resolution and nearly 20% lower reconstruction root-mean-square-error (RMSE) as compared to the un-optimized imaging system. For the iris-recognition task, the optimized imaging system achieves a 33% improvement in false rejection ratio (FRR) for a fixed alarm ratio (FAR) relative to the conventional imaging system. The effect of the performance measures like resolution, RMSE, FRR, and FAR on the optimal design highlights the crucial role of task-specific design metrics in the JO framework. We introduce a fundamental measure of task-specific performance known as task-specific information (TSI), an information-theoretic measure that quantifies the information content of an image measurement relevant to a specific task. A variety of source-models are derived to illustrate the application of a TSI-based analysis to conventional and compressive imaging (CI) systems for various tasks such as target detection and classification. A TSI-based design and optimization framework is also developed and applied to the design of CI systems for the task of target detection, it yields a six-fold performance improvement over the conventional imaging system at low signal-to-noise ratios.

Author : Ayush Bhandari
Publisher : MIT Press
Page : 482 pages
File Size : 36,48 MB
Release : 2022-10-25
Category : Technology & Engineering
ISBN : 0262046474

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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 : Charles A. Bouman
Publisher : SIAM
Page : 350 pages
File Size : 20,23 MB
Release : 2022-07-06
Category : Mathematics
ISBN : 1611977134

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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 : Ayush Bhandari
Publisher : MIT Press
Page : 482 pages
File Size : 20,52 MB
Release : 2022-10-25
Category : Technology & Engineering
ISBN : 0262368374

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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 : Joseph Van der Gracht
Publisher :
Page : 246 pages
File Size : 10,8 MB
Release : 2002
Category : Technology & Engineering
ISBN :

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Digest and expanded papers from a November 2001 meeting offer definitions of integrated imaging, present examples of imaging systems, and describe concepts from information theory as they apply to the analysis and design of imaging systems. Material is in sections on key topics, wavefront coding, computational microscopes, information theory and design, imaging systems, implementation, hyperspectral systems, and analysis and situation. Three-dimensional coherence imaging in the Fresnel domain, spatial tomography and coherence microscopy, and modeling of sparse aperture telescope image quality are some of the areas discussed. Annotation copyrighted by Book News, Inc., Portland, OR

Author : Ronan Kerviche
Publisher :
Page : pages
File Size : 47,58 MB
Release : 2017
Category :
ISBN :

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Computational imaging and sensing leverages the joint-design of optics, detectors and processing to overcome the performance bottlenecks inherent to the traditional imaging paradigm. This novel imaging and sensing design paradigm essentially allows new trade-offs between the optics, detector and processing components of an imaging system and enables broader operational regimes beyond the reach of conventional imaging architectures, which are constrained by well-known Rayleigh, Strehl and Nyquist rules amongst others. In this dissertation, we focus on scalability aspects of these novel computational imaging architectures, their design and implementation, which have far-reaching impacts on the potential and feasibility of realizing task-specific performance gains relative to traditional imager designs. For the extended depth of field (EDoF) computational imager design, which employs a customized phase mask to achieve defocus immunity, we propose a joint-optimization framework to simultaneously optimize the parameters of the optical phase mask and the processing algorithm, with the system design goal of minimizing the noise and artifacts in the final processed image. Using an experimental prototype, we demonstrate that our optimized system design achieves higher fidelity output compared to other static designs from the literature, such as the Cubic and Trefoil phase masks. While traditional imagers rely on an isomorphic mapping between the scene and the optical measurements to form images, they do not exploit the inherent compressibility of natural images and thus are subject to Nyquist sampling. Compressive sensing exploits the inherent redundancy of natural images, basis of image compression algorithms like JPEG/JPEG2000, to make linear projection measurements with far fewer samples than Nyquist for the image forming task. Here, we present a block wise compressive imaging architecture which is scalable to high space-bandwidth products (i.e. large FOV and high resolution applications) and employs a parallelizable and non-iterative piecewise linear reconstruction algorithm capable of operating in real-time. Our compressive imager based on this scalable architecture design is not limited to the imaging task and can also be used for automatic target recognition (ATR) without an intermediate image reconstruction. To maximize the detection and classification performance of this compressive ATR sensor, we have developed a scalable statistical model of natural scenes, which enables the optimization of the compressive sensor projections with the Cauchy-Schwarz mutual information metric. We demonstrate the superior performance of this compressive ATR system using simulation and experiment. Finally, we investigate the fundamental resolution limit of imaging via the canonical incoherent quasi-monochromatic two point-sources separation problem. We extend recent results in the literature demonstrating, with Fisher information and estimator mean square error analysis, that a passive optical mode-sorting architecture with only two measurements can outperform traditional intensity-based imagers employing an ideal focal plane array in the sub-Rayleigh range, thus overcoming the Rayleigh resolution limit.

Author :
Publisher :
Page : 316 pages
File Size : 49,36 MB
Release : 2005
Category : Dissertations, Academic
ISBN :

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"Education, arts and social sciences, natural and technical sciences in the United States and Canada".