Multi-dimensional Computational Imaging from Diffraction Intensity Using Deep Neural Networks PDF Download
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Author: Iksung Kang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Diffraction of light can be found everywhere in nature, from sunlight rays fanning out from clouds to multiple colors reflected from the surface of a CD. This phenomenon of light explains any change in the path of light due to an obstacle and is of particular significance as it allows us to see transparent (or pure-phase) objects, e.g. biological cells under visible-wavelength light or integrated circuits under X-rays, with proper exploitation of the phenomenon. However, cameras only measure the intensity of the diffracted light, which makes the camera measurements incomplete due to the loss of phase information. Thus, this thesis addresses the reconstruction of multi-dimensional phase information from diffraction intensities with a regularized inversion using deep neural networks for two- and three-dimensional applications. The inversion process begins with the definition of a forward physical model that relates a diffraction intensity to a phase object and then involves a physics-informing step (or equivalently, physics prior) to deep neural networks, if applicable. In this thesis, two-dimensional wavefront aberrations are retrieved for high-contrast imaging of exoplanets using a deep residual neural network, and transparent planar objects behind dynamic scattering media are revealed by a recurrent neural network, both in an end-to-end training fashion. Next, a multi-layered, three-dimensional glass phantom of integrated circuits is reconstructed under the limited-angle phase computed tomography geometry with visible-wavelength laser illumination using a dynamical machine learning framework. Furthermore, a deep neural network regularization is deployed for the reconstruction of real integrated circuits from far-field diffraction intensities under the ptychographic X-ray computed tomography geometry with partially coherent synchrotron X-ray illumination.
Author: Iksung Kang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Diffraction of light can be found everywhere in nature, from sunlight rays fanning out from clouds to multiple colors reflected from the surface of a CD. This phenomenon of light explains any change in the path of light due to an obstacle and is of particular significance as it allows us to see transparent (or pure-phase) objects, e.g. biological cells under visible-wavelength light or integrated circuits under X-rays, with proper exploitation of the phenomenon. However, cameras only measure the intensity of the diffracted light, which makes the camera measurements incomplete due to the loss of phase information. Thus, this thesis addresses the reconstruction of multi-dimensional phase information from diffraction intensities with a regularized inversion using deep neural networks for two- and three-dimensional applications. The inversion process begins with the definition of a forward physical model that relates a diffraction intensity to a phase object and then involves a physics-informing step (or equivalently, physics prior) to deep neural networks, if applicable. In this thesis, two-dimensional wavefront aberrations are retrieved for high-contrast imaging of exoplanets using a deep residual neural network, and transparent planar objects behind dynamic scattering media are revealed by a recurrent neural network, both in an end-to-end training fashion. Next, a multi-layered, three-dimensional glass phantom of integrated circuits is reconstructed under the limited-angle phase computed tomography geometry with visible-wavelength laser illumination using a dynamical machine learning framework. Furthermore, a deep neural network regularization is deployed for the reconstruction of real integrated circuits from far-field diffraction intensities under the ptychographic X-ray computed tomography geometry with partially coherent synchrotron X-ray illumination.
Author: Calvin Brown
Publisher:
ISBN:
Category :
Languages : en
Pages : 117
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Book Description
Computational imaging and sensing aim to redesign optical systems from the ground up, jointly considering both hardware/sensors and software/reconstruction algorithms to enable new modalities with superior capabilities, speed, cost, and/or footprint. Often systems can be optimized with targeted applications in mind, such as low-light imaging or remote sensing in a specific spectral regime. For medical diagnostics in particular, computational sensing could enable more portable, cost-effective systems and in turn improve access to care. In the last decade, the increased availability of data and cost-effective computational resources coupled with the commodification of neural networks has accelerated and expanded the potential for these computational sensing systems.First, I will present my work on a cost-effective system for quantifying antimicrobial resistance, which could be of particular use in resource-limited settings, where poverty, population density, and lack of healthcare infrastructure lead to the emergence of some of the most resistant strains of bacteria. The device uses optical fibers to spatially subsample all 96 wells of a standard microplate without any scanning components, and a neural network identifies bacterial growth from the optical intensity information captured by the fibers. Our accelerated antimicrobial susceptibility testing system can interface with the current laboratory workflow and, when blindly tested on patient bacteria at UCLA Health, was able to identify bacterial growth after an average of 5.72 h, as opposed to the gold standard method requiring 18-24 h. The system is completely automated, avoiding the need for a trained medical technologist to manually inspect each well of a standard 96-well microplate for growth. Second, I will discuss a deep learning-enabled spectrometer framework using localized surface plasmon resonance. By fabricating an array of periodic nanostructures with varying geometries, we created a "spectral encoder chip" whose spatial transmission intensity depends upon the incident spectrum of light. A neural network uses the transmitted intensities captured by a CMOS image sensor to faithfully reconstruct the underlying spectrum. Unlike conventional diffraction-based spectrometers, this framework is scalable to large areas through imprint lithography, conducive to compact, lightweight designs, and, crucially, does not suffer from the resolution-signal strength tradeoff inherent to grating-based designs.
Author: Tijmen Jan Moser
Publisher: SEG Books
ISBN: 1560803177
Category : Science
Languages : en
Pages : 823
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Book Description
The use of diffraction imaging to complement the seismic reflection method is rapidly gaining momentum in the oil and gas industry. As the industry moves toward exploiting smaller and more complex conventional reservoirs and extensive new unconventional resource plays, the application of the seismic diffraction method to image sub-wavelength features such as small-scale faults, fractures and stratigraphic pinchouts is expected to increase dramatically over the next few years. “Seismic Diffraction” covers seismic diffraction theory, modeling, observation, and imaging. Papers and discussion include an overview of seismic diffractions, including classic papers which introduced the potential of diffraction phenomena in seismic processing; papers on the forward modeling of seismic diffractions, with an emphasis on the theoretical principles; papers which describe techniques for diffraction mathematical modeling as well as laboratory experiments for the physical modeling of diffractions; key papers dealing with the observation of seismic diffractions, in near-surface-, reservoir-, as well as crustal studies; and key papers on diffraction imaging.
Author: Lakhmi C. Jain
Publisher: Springer Nature
ISBN: 9811631808
Category : Technology & Engineering
Languages : en
Pages : 332
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Book Description
This book presents high-quality research in the field of 3D imaging technology. The second edition of International Conference on 3D Imaging Technology (3DDIT-MSP&DL) continues the good traditions already established by the first 3DIT conference (IC3DIT2019) to provide a wide scientific forum for researchers, academia and practitioners to exchange newest ideas and recent achievements in all aspects of image processing and analysis, together with their contemporary applications. The conference proceedings are published in 2 volumes. The main topics of the papers comprise famous trends as: 3D image representation, 3D image technology, 3D images and graphics, and computing and 3D information technology. In these proceedings, special attention is paid at the 3D tensor image representation, the 3D content generation technologies, big data analysis, and also deep learning, artificial intelligence, the 3D image analysis and video understanding, the 3D virtual and augmented reality, and many related areas. The first volume contains papers in 3D image processing, transforms and technologies. The second volume is about computing and information technologies, computer images and graphics and related applications. The two volumes of the book cover a wide area of the aspects of the contemporary multidimensional imaging and the related future trends from data acquisition to real-world applications based on various techniques and theoretical approaches.
Author: Bijie Bai
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The rapid development of machine learning has transformed conventional optical imaging processes, setting new benchmarks in computational imaging tasks. In this dissertation, we delve into the transformative impact of recent advancements in machine learning on optical imaging processes, focusing on how these technologies revolutionize computational imaging tasks. Specifically, this dissertation centers on two major topics: deep learning-enabled computational microscopy and the all-optical diffractive networks. Optical microscopy has long served as the benchmark technique for diagnosing various diseases over centuries. However, its reliance on high-end optical components and accessories, necessary to adapt to various imaging samples and conditions, often limits its applicability and throughput. Recent advancements in computational imaging techniques utilizing deep learning methods have transformed conventional microscopic imaging modalities, delivering both enhanced speed and superior image quality without introducing extra complexity of the optical systems. In the first topic of this dissertation, we demonstrate that deep learning-enabled image translation approach can significantly benefit a wide range of applications for microscopic imaging. We start with introducing a customized system for single-shot quantitative polarization imaging, capable of reconstructing comprehensive birefringent maps from a single image capture, which offers enhanced sensitivity and specificity in diagnosing crystal-induced diseases. Utilizing these quantitative birefringent maps as a baseline, we employ deep learning tools to convert phase-recovered holograms into quantitative birefringence maps, thereby improving the throughput of crystal detection with simplified system complexity. Extending this concept of deep learning-enabled image translation, we also explore its applications in histopathology. Our technique, termed as "virtual histological staining", transforms unstained biological samples into visually rich, stained-like images without the need for chemical agents. This innovation minimizes costs, labor, and diagnostic delays as well as opens up new possibilities in histopathology workflow. The evolution of deep learning tools not only facilities the optical image analysis and processing, but also provides guidance in design and enhancement of optical systems. The second topic of this dissertation is the development and application of diffractive deep neural networks (D2NN). Developed with deep learning, D2NNs execute given computational tasks by manipulating light diffraction through a series of engineered surfaces, which is completed at the speed of light propagation with negligible power consumption. Based on this framework, a lot of novel computational tasks can be executed in an all-optical way, which is beyond the capabilities of the traditional optics design approaches. We introduce several all-optical computational imaging applications based on D2NN, including class-specific imaging, class-specific image encryption, and unidirectional image magnification and demagnification, demonstrating the versatility of this promising framework.
Author: Lakhmi C. Jain
Publisher: Springer Nature
ISBN: 9811924481
Category : Technology & Engineering
Languages : en
Pages : 262
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Book Description
This book gathers selected papers presented at the conference “Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology,” one of the first initiatives devoted to the problems of 3D imaging in all contemporary scientific and application areas. The two volumes of the book cover wide area of the aspects of the contemporary multidimensional imaging and outline the related future trends from data acquisition to real-world applications based on new techniques and theoretical approaches. This volume contains papers devoted to the theoretical representation and analysis of the 3D images. The related topics included are 3D image transformation, 3D tensor image representation, 3D content generation technologies, 3D graphic information processing, VR content generation technologies, multi-dimensional image processing, dynamic and auxiliary 3D displays, VR/AR/MR device, VR camera technologies, 3D imaging technologies and applications, 3D computer vision, 3D video communications, 3D medical images processing and analysis, 3D remote sensing images and systems, deep learning for image restoration and recognition, neural networks for MD image processing, etc.
Author: Lakhmi C. Jain
Publisher: Springer Nature
ISBN: 9811633916
Category : Technology & Engineering
Languages : en
Pages : 341
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Book Description
This book presents high-quality research in the field of 3D imaging technology. The second edition of International Conference on 3D Imaging Technology (3DDIT-MSP&DL) continues the good traditions already established by the first 3DIT conference (IC3DIT2019) to provide a wide scientific forum for researchers, academia and practitioners to exchange newest ideas and recent achievements in all aspects of image processing and analysis, together with their contemporary applications. The conference proceedings are published in 2 volumes. The main topics of the papers comprise famous trends as: 3D image representation, 3D image technology, 3D images and graphics, and computing and 3D information technology. In these proceedings, special attention is paid at the 3D tensor image representation, the 3D content generation technologies, big data analysis, and also deep learning, artificial intelligence, the 3D image analysis and video understanding, the 3D virtual and augmented reality, and many related areas. The first volume contains papers in 3D image processing, transforms and technologies. The second volume is about computing and information technologies, computer images and graphics and related applications. The two volumes of the book cover a wide area of the aspects of the contemporary multidimensional imaging and the related future trends from data acquisition to real-world applications based on various techniques and theoretical approaches.
Author: Shuai Li (Ph.D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 154
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Book Description
Computational imaging (CI) is a class of imaging systems that uses inverse algorithms to recover an unknown object from the physical measurement. Traditional inverse algorithms in CI obtain an estimate of the object by minimizing the Tikhonov functional, which requires explicit formulations of the forward operator of the physical system, as well as the prior knowledge about the class of objects being imaged. In recent years, machine learning architectures, and deep learning (DL) in particular, have attracted increasing attentions from CI researchers. Unlike traditional inverse algorithms in CI, DL approach learns both the forward operator and the objects’ prior implicitly from training examples. Therefore, it is especially attractive when the forward imaging model is uncertain (e.g. imaging through random scattering media), or the prior about the class of objects is difficult to be expressed analytically (e.g. natural images). In this thesis, the application of DL approaches in two different CI scenarios are investigated: imaging through a glass diffuser and quantitative phase retrieval (QPR), where an Imaging through Diffuser Network (IDiffNet) and a Phase Extraction Neural Network (PhENN) are experimentally demonstrated, respectively. This thesis also studies the influences of the two main factors that determine the performance of a trained neural network: network architecture (connectivity, network depth, etc) and training example quality (spatial frequency content in particular). Motivated by the analysis of the latter factor, two novel approaches, spectral pre-modulation approach and Learning Synthesis by DNN (LS-DNN) method, are successively proposed to improve the visual qualities of the network outputs. Finally, the LS-DNN enhanced PhENN is applied to a phase microscope to recover the phase of a red blood cell (RBC) sample. Furthermore, through simulation of the learned weak object transfer function (WOTF) and experiment on a star-like phase target, we demonstrate that our network has indeed learned the correct physical model rather than doing something trivial as pattern matching.
Author: Srikanta Patnaik
Publisher: Springer Nature
ISBN: 9819911451
Category : Technology & Engineering
Languages : en
Pages : 283
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Book Description
This book presents high-quality research in the field of 3D imaging technology. The fourth edition of International Conference on 3D Imaging Technology (3DDIT-MSP&DL) continues the good traditions already established by the first three editions of the conference to provide a wide scientific forum for researchers, academia and practitioners to exchange newest ideas and recent achievements in all aspects of image processing and analysis, together with their contemporary applications. The conference proceedings are published in 2 volumes. The main topics of the papers comprise famous trends as: 3D image representation, 3D image technology, 3D images and graphics, and computing and 3D information technology. In these proceedings, special attention is paid at the 3D tensor image representation, the 3D content generation technologies, big data analysis, and also deep learning, artificial intelligence, the 3D image analysis and video understanding, the 3D virtual and augmented reality, and many related areas. The first volume contains papers in 3D image processing, transforms and technologies. The second volume is about computing and information technologies, computer images and graphics and related applications. The two volumes of the book cover a wide area of the aspects of the contemporary multidimensional imaging and the related future trends from data acquisition to real-world applications based on various techniques and theoretical approaches.
Author: Lakhmi C. Jain
Publisher: Springer Nature
ISBN: 981192452X
Category : Technology & Engineering
Languages : en
Pages : 237
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Book Description
This book gathers selected papers presented at the conference “Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology,” one of the first initiatives devoted to the problems of 3D imaging in all contemporary scientific and application areas. The two volumes of the book cover wide area of the aspects of the contemporary multidimensional imaging and outline the related future trends from data acquisition to real-world applications based on new techniques and theoretical approaches. This volume contains papers aimed at the multidimensional systems and signal processing, deep learning, mathematical approaches and the related applications. The related topics are multidimensional multi-component image processing; multidimensional image representation and super-resolution; compression of multidimensional spatio-temporal images; multidimensional image transmission systems; multidimensional signal processing; prediction and filtering of multidimensional process; intelligent multi-spectral and hyper-spectral image processing, intelligent multi-view image processing, 3D deep learning, 3D GIS and graphic database; data-based MD image retrieval and knowledge data mining; watermarking, hiding and encryption of MD images; intelligent visualization of MD images; forensic analysis systems for M3D graphics algorithm; 3D VR (Virtual Reality)/AR (Augmented Reality); applications of multidimensional signal processing; applications of multidimensional systems; multidimensional filters and filter-banks.
