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Author: Ronan Kerviche Publisher: ISBN: Category : Languages : en Pages :
Book Description
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: Ronan Kerviche Publisher: ISBN: Category : Languages : en Pages :
Book Description
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: Ayush Bhandari Publisher: MIT Press ISBN: 0262368374 Category : Technology & Engineering Languages : en Pages : 482
Book Description
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: Amit Ashok Publisher: ISBN: Category : Languages : en Pages : 356
Book Description
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: Joseph Van der Gracht Publisher: ISBN: Category : Technology & Engineering Languages : en Pages : 246
Book Description
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: Cheng Liu Publisher: Springer Nature ISBN: 9811916411 Category : Science Languages : en Pages : 311
Book Description
In this book, computational optical phase imaging techniques are presented along with Matlab codes that allow the reader to run their own simulations and gain a thorough understanding of the current state-of-the-art. The book focuses on modern applications of computational optical phase imaging in engineering measurements and biomedical imaging. Additionally, it discusses the future of computational optical phase imaging, especially in terms of system miniaturization and deep learning-based phase retrieval.
Author: Wai-San Chan Publisher: Open Dissertation Press ISBN: 9781361470923 Category : Languages : en Pages :
Book Description
This dissertation, "Design and Analysis of Integrated Computational Imaging Systems" by Wai-san, Chan, 陳慧珊, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled "Design and Analysis of Integrated Computational Imaging Systems" Submitted by Chan Wai San for the degree of Master of Philosophy at The University of Hong Kong in September 2007 In an integrated computational imaging system, the physical system which gen- erates the image signal, data collection and post-processing are integrally incor- porated in the design process. This system usually does not deliver a visually pleasingimageatthefirststep. Instead, itproducesanintermediateimagewhich, although not visually attractive, preserves all the useful information of the ob- ject. Post-detection computation of the intermediate image will lead to a better final image. The motivation of integrated computational imaging systems is that through the concurrent design and joint optimization of signal generation, data collection and post-processing, performance and efficiency can be enhanced. This dissertation investigates two integrated computational imaging systems. The first is a compound-eye imaging (CEI) system, and the second is a magnetic resonance imaging (MRI) system. The CEI system is a non-conventional optical imagingsystemwhichismadeverycompactbytheutilizationofanarrayofsmall lenses in image formation. The array of small lenses forms an intermediate imagewhich consists of multiple low-resolution sub-images of the object. The final image is recovered by post-processing of the multiple sub-images. Low resolution and poor quality of the reconstructed image are the main problems of a CEI system. In this study, we use our own super-resolution algorithm for image reconstruction for a CEI system to enhance the quality and resolution of the reconstructed image. The capability of the system is further enhanced by the incorporation of a phase-mask array to increase its depth of field. A virtual CEI system was built to facilitate the investigation. The feasibilities of our proposed methods are verified by simulation experiments with the virtual system. The second part of the study investigates an MRI system. MRI is a powerful medical imaging module. However, the long scan time is an impediment to its use in certain applications. This study examines the feasibility and efficiency of applying compressive sensing (CS) in MRI to reduce the scan time. It also explores how k-space data acquisitions affect the performance of CS on MRI reconstruction. The analysis is based on simulation experiments. The study's findings indicate that the conventional radial and spiral trajectories are both robustk-spacemeasurementschemeswhichcanworkwellwithCSreconstruction to give high quality MR images from a highly incomplete set (just around 13%) of k-space data. An abstract of exactly 383 words DOI: 10.5353/th_b3896035 Subjects: Imaging systems - Design and construction Image processing - Digital techniques Magnetic resonance imaging
Author: Gerald C. Holst Publisher: SPIE-International Society for Optical Engineering ISBN: 9780819437013 Category : Electrooptical devices Languages : en Pages : 438
Book Description
Copublished with JCD Publishing. Infrared imaging applications have exploded since the first edition was published in 1995. From a modeling point-of-view, several significant changes have occurred. Uncooled technology is based upon microbolometer and pyroelectric detectors. Systems with novel semiconductors such as quantum well detectors are routinely produced. These detectors, along with their characteristics, are described in this edition.
Author: Lakshminarayan Hazra Publisher: CRC Press ISBN: 1498744958 Category : Science Languages : en Pages : 775
Book Description
Since the incorporation of scientific approach in tackling problems of optical instrumentation, analysis and design of optical systems constitute a core area of optical engineering. A large number of software with varying level of scope and applicability is currently available to facilitate the task. However, possession of an optical design software, per se, is no guarantee for arriving at correct or optimal solutions. The validity and/or optimality of the solutions depend to a large extent on proper formulation of the problem, which calls for correct application of principles and theories of optical engineering. On a different note, development of proper experimental setups for investigations in the burgeoning field of optics and photonics calls for a good understanding of these principles and theories. With this backdrop in view, this book presents a holistic treatment of topics like paraxial analysis, aberration theory, Hamiltonian optics, ray-optical and wave-optical theories of image formation, Fourier optics, structural design, lens design optimization, global optimization etc. Proper stress is given on exposition of the foundations. The proposed book is designed to provide adequate material for ‘self-learning’ the subject. For practitioners in related fields, this book is a handy reference. Foundations of Optical System Analysis and Synthesis provides A holistic approach to lens system analysis and design with stress on foundations Basic knowledge of ray and wave optics for tackling problems of instrumental optics Proper explanation of approximations made at different stages Sufficient illustrations for facilitation of understanding Techniques for reducing the role of heuristics and empiricism in optical/lens design A sourcebook on chronological development of related topics across the globe This book is composed as a reference book for graduate students, researchers, faculty, scientists and technologists in R & D centres and industry, in pursuance of their understanding of related topics and concepts during problem solving in the broad areas of optical, electro-optical and photonic system analysis and design.
Author: Kedar Khare Publisher: Springer Nature ISBN: 3031183533 Category : Technology & Engineering Languages : en Pages : 295
Book Description
The book is designed to serve as a textbook for advanced undergraduate and graduate students enrolled in physics and electronics and communication engineering and mathematics. The book provides an introduction to Fourier optics in light of new developments in the area of computational imaging over the last couple of decades. There is an in-depth discussion of mathematical methods such as Fourier analysis, linear systems theory, random processes, and optimization-based image reconstruction techniques. These techniques are very much essential for a better understanding of the working of computational imaging systems. It discusses topics in Fourier optics, e.g., diffraction phenomena, coherent and incoherent imaging systems, and some aspects of coherence theory. These concepts are then used to describe several system ideas that combine optical hardware design and image reconstruction algorithms, such as digital holography, iterative phase retrieval, super-resolution imaging, point spread function engineering for enhanced depth-of-focus, projection-based imaging, single-pixel or ghost imaging, etc. The topics covered in this book can provide an elementary introduction to the exciting area of computational imaging for students who may wish to work with imaging systems in their future careers.
Author: Ronan Kerviche
Author: Ronan Kerviche
Author: Ayush Bhandari
Author: Amit Ashok
Author: Joseph Van der Gracht
Author: Cheng Liu
Author: 
Author: Wai-San Chan
Author: Gerald C. Holst
Author: Lakshminarayan Hazra
Author: Kedar Khare