Author: Shuai Li (Ph.D.) Publisher: ISBN: Category : Languages : en Pages : 154
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: Nicu Sebe Publisher: Springer Science & Business Media ISBN: 1402032757 Category : Computers Languages : en Pages : 253
Book Description
The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.
Author: Calvin Brown Publisher: ISBN: Category : Languages : en Pages : 117
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: Yichen Wu Publisher: ISBN: Category : Languages : en Pages : 182
Book Description
Exponential advancements in computational resources and algorithms have given birth to the new paradigm in imaging that rely on computation to digitally reconstruct and enhance images. These computational imaging modalities have enabled higher resolution, larger throughput and/or automatic detection capabilities for optical microscopy. An example is lens-less digital holographic microscope, which enables snapshot imaging of volumetric samples over wide field-of-view without using imaging lenses. Recent developments in the field of deep learning have further opened up exciting avenues for computational imaging, which offer unprecedented performance thanks to their capability to robustly learn content-specific complex image priors. This dissertation introduces a novel and universal modeling framework of deep learning -based image reconstruction technique to tackle various challenges in optical microscopic imaging, including digital holography reconstructions and 3D fluorescence microscopy. Firstly, auto-focusing and phase recovery in holography reconstruction are conventionally challenging and time-consuming to digitally perform. A convolutional neural network (CNN) based approach was developed that solves both problems rapidly in parallel, enabling extended depth-of-field holographic reconstruction with significantly improved time complexity from O(mn) to O(1). Secondly, to fuse advantages of snapshot volumetric capability in digital holography and speckle- and artifact-free image contrast in bright-field microscopy, a CNN was used to transform across microscopy modalities from holographic image reconstructions to their equivalent high contrast bright-field microscopic images. Thirdly, 3D fluorescence microscopy generally requires axial scanning. A CNN was trained to learn defocuses of fluorescence and digitally refocusing a single 2D fluorescence image onto user-defined 3D surfaces within the sample volume, which extends depth-of-field of fluorescence microscopy by 20-fold without any axial scanning, additional hardware, or a trade-off of imaging resolution or speed. This enables high-speed volumetric imaging and digital aberration correction for live samples. Based on deep learning powered computational microscopy, a hand-held device was also developed to measure the particulate matters and bio-aerosols in the air using the lens-less digital holographic microscopic imaging geometry. This device, named c-Air, demonstrates accurate, high-throughput and automatic detection, sizing and classification of the particles in the air, which opens new opportunities in deep learning based environmental sensing and personalized and/or distributed air quality monitoring.
Author: Jong Chul Ye Publisher: Cambridge University Press ISBN: 1316517519 Category : Technology & Engineering Languages : en Pages : 365
Book Description
Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications. The background theory of deep learning is introduced step-by-step, and by incorporating modeling fundamentals this book explains how to implement deep learning in a variety of modalities, including X-ray, CT, MRI and others. Real-world examples demonstrate an interdisciplinary approach to medical image reconstruction processes, featuring numerous imaging applications. Recent clinical studies and innovative research activity in generative models and mathematical theory will inspire the reader towards new frontiers. This book is ideal for graduate students in Electrical or Biomedical Engineering or Medical Physics.
Author: Rastislav Lukac Publisher: CRC Press ISBN: 1439817502 Category : Computers Languages : en Pages : 564
Book Description
Computational photography refers broadly to imaging techniques that enhance or extend the capabilities of digital photography. This new and rapidly developing research field has evolved from computer vision, image processing, computer graphics and applied optics—and numerous commercial products capitalizing on its principles have already appeared in diverse market applications, due to the gradual migration of computational algorithms from computers to imaging devices and software. Computational Photography: Methods and Applications provides a strong, fundamental understanding of theory and methods, and a foundation upon which to build solutions for many of today's most interesting and challenging computational imaging problems. Elucidating cutting-edge advances and applications in digital imaging, camera image processing, and computational photography, with a focus on related research challenges, this book: Describes single capture image fusion technology for consumer digital cameras Discusses the steps in a camera image processing pipeline, such as visual data compression, color correction and enhancement, denoising, demosaicking, super-resolution reconstruction, deblurring, and high dynamic range imaging Covers shadow detection for surveillance applications, camera-driven document rectification, bilateral filtering and its applications, and painterly rendering of digital images Presents machine-learning methods for automatic image colorization and digital face beautification Explores light field acquisition and processing, space-time light field rendering, and dynamic view synthesis with an array of cameras Because of the urgent challenges associated with emerging digital camera applications, image processing methods for computational photography are of paramount importance to research and development in the imaging community. Presenting the work of leading experts, and edited by a renowned authority in digital color imaging and camera image processing, this book considers the rapid developments in this area and addresses very particular research and application problems. It is ideal as a stand-alone professional reference for design and implementation of digital image and video processing tasks, and it can also be used to support graduate courses in computer vision, digital imaging, visual data processing, and computer graphics, among others.
Author: Bijie Bai Publisher: ISBN: Category : Languages : en Pages : 0
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: Le Lu Publisher: Springer ISBN: 331942999X Category : Computers Languages : en Pages : 327
Book Description
This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Author: Ayush Bhandari Publisher: MIT Press ISBN: 0262046474 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: S. Kevin Zhou Publisher: Academic Press ISBN: 0323858880 Category : Computers Languages : en Pages : 544
Book Description
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
Author: Shuai Li (Ph.D.)
Author: Nicu Sebe
Author: Calvin Brown
Author: Yichen Wu
Author: Jong Chul Ye
Author: Rastislav Lukac
Author: Bijie Bai
Author: Le Lu
Author: Ayush Bhandari
Author: S. Kevin Zhou