3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model PDF Download
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Author: Morteza Hasani Shoreh
Publisher:
ISBN:
Category :
Languages : en
Pages : 108
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Book Description
Mots-clés de l'auteur: Optical diffraction tomography ; 3D refractive index reconstruction ; neural network ; interference microscopy ; biomedical imaging ; digital holography ; image reconstruction techniques ; beam propagation method ; total variation regularization ; inverse problems.
Author: Morteza Hasani Shoreh
Publisher:
ISBN:
Category :
Languages : en
Pages : 108
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Book Description
Mots-clés de l'auteur: Optical diffraction tomography ; 3D refractive index reconstruction ; neural network ; interference microscopy ; biomedical imaging ; digital holography ; image reconstruction techniques ; beam propagation method ; total variation regularization ; inverse problems.
Author:
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ISBN:
Category :
Languages : zh-CN
Pages :
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Author: Jinyang Liang
Publisher: Springer Nature
ISBN: 3031390628
Category :
Languages : en
Pages : 697
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Book Description
Author: Mohammad-Taghi H. Maleki
Publisher:
ISBN:
Category : Scattering (Mathematics)
Languages : en
Pages : 246
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Book Description
Author: Gabor T. Herman
Publisher: Springer Science & Business Media
ISBN: 1461495210
Category : Mathematics
Languages : en
Pages : 275
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Book Description
Approaches to the recovery of three-dimensional information on a biological object, which are often formulated or implemented initially in an intuitive way, are concisely described here based on physical models of the object and the image-formation process. Both three-dimensional electron microscopy and X-ray tomography can be captured in the same mathematical framework, leading to closely-related computational approaches, but the methodologies differ in detail and hence pose different challenges. The editors of this volume, Gabor T. Herman and Joachim Frank, are experts in the respective methodologies and present research at the forefront of biological imaging and structural biology. Computational Methods for Three-Dimensional Microscopy Reconstruction will serve as a useful resource for scholars interested in the development of computational methods for structural biology and cell biology, particularly in the area of 3D imaging and modeling.
Author:
Publisher:
ISBN:
Category : Electrical engineering
Languages : en
Pages : 1360
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Author: Fengting Yang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Reconstructing 3D models from given 2D images is one of the most fundamental problems in computer vision. Most traditional 3D reconstruction algorithms focus on geometric knowledge and attempt to tackle this problem with hand-craft features. However, these algorithms are usually fragile if the images contain noisy, textureless, or repetitive patterns. On the contrary, recent deep neural network-based methods rely on the image patterns and learn 3D information (e.g., depth and normal) in a data-driven manner. Without explicit geometric knowledge, these networks often suffer from performance drop once being applied to environments that are significantly different from the training ones. In this dissertation, we ask the question of whether we can address these shortcomings by combining the merits of traditional geometry-based methods and recent data-driven-based methods. To answer this question, we explore four popular 3D reconstruction tasks: (1) single-view 3D reconstruction, (2) stereo matching, (3) multi-view stereo (MVS), and (4) depth-from-focus (DFF). In each task, we take the deep neural network as the basic framework and integrate task-specific geometric knowledge into the network design. More specifically, in single-view reconstruction, we introduce plane regularity into the network and propose a structure-induced loss to train the network to recover 3D planes without supervision from ground truth plane annotation. In stereo matching, we apply the piecewise plane model to the network to better preserve object boundaries and fine details. A fully convolutional network-based superpixel segmentation approach is developed, and we incorporate it with an existing stereo matching network by considering each superpixel represents a projection of a slanted plane in the scene. In MVS, we integrate two common indoor priors into a truncated sign distance function (TSDF) regression network for indoor multi-view reconstruction. Finally, in DFF, we consider the special projective geometry of the defocus system and propose a deep differential focus volume for the DFF network. By developing these geometry-inspired networks for various tasks, we validate the effectiveness of integrating geometry with deep networks and provide an important stepping stone toward high-performance 3D reconstruction methods in multiple application settings.
Author: Voulodimos, Athanasios
Publisher: IGI Global
ISBN: 1522552952
Category : Computers
Languages : en
Pages : 396
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Book Description
3D image reconstruction is used in many fields, such as medicine, entertainment, and computer science. This highly demanded process comes with many challenges, such as images becoming blurry by atmospheric turbulence, getting snowed with noise, or becoming damaged within foreign regions. It is imperative to remain well-informed with the latest research in this field. Recent Advances in 3D Imaging, Modeling, and Reconstruction is a collection of innovative research on the methods and common techniques of image reconstruction as well as the accuracy of these methods. Featuring coverage on a wide range of topics such as ray casting, holographic techniques, and machine learning, this publication is ideally designed for graphic designers, computer engineers, medical professionals, robotics engineers, city planners, game developers, researchers, academicians, and students.
Author: Laure Montandon-Varoda
Publisher:
ISBN:
Category :
Languages : en
Pages : 219
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Book Description
Author: Iksung Kang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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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.
