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检索条件"主题词=Reconstruction algorithm"

共 1040 条记录，以下是851-860 订阅

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COMPRESSIVE COMPUTED TOMOGRAPHY IMAGE reconstruction WITH DENOISING MESSAGE PASSING algorithmS

COMPRESSIVE COMPUTED TOMOGRAPHY IMAGE RECONSTRUCTION WITH DE...

引用

European Signal Processing Conference

作者： Alessandro Perelli Mike E. Davies Institute for Digital Communications (IDCOM) University of Edinburgh

ISBN: (纸本)9781479988518

In this paper we address the compressive reconstruction of images from a limited number of projections in order to reduce the X-ray radiation dose in Computed Tomography (CT) while achieving high diagnostic performances. Our objective is to study the feasibility of applying message passing Compressive Sensing (CS) imaging algorithms to CT image reconstruction extending the algorithm from its theoretical domain of i.i.d. random matrices. Exploiting the intuition described in [1] of employing a generic denoiser in a CS reconstruction algorithm, we propose a denoising-based Turbo CS algorithm (D-Turbo) and we extend the application of the denoising approximate message passing (D-AMP) algorithm to partial Radon Projection data with a Gaussian approximation of the Poisson noise model. The proposed CS message passing approaches have been tested on simulated CT data using the BM3D denoiser [2] yielding an improvement in the reconstruction quality compared to existing direct and iterative methods. The promising results show the effectiveness of the idea to employ a generic denoiser Turbo CS or message passing algorithm for reduced number of views CT reconstruction.

关键词： Computed Tomography Radon Transform Approximate Message Passing Turbo Compressed Sensing X-Ray Computed Tomography image reconstruction Radon transforms message passing reconstruction algorithm Gaussian approximation carbon monosulfide CONTAINMENT SPRAY Cesium algorithms conditioned stimulus Intuition

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Variational Bayesian Image reconstruction with an Uncertainty Model for Measurement Localization

Variational Bayesian Image Reconstruction with an Uncertaint...

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European Signal Processing Conference

作者： Filip Sroubek Jindrich Soukup Barbara Zitova Institute of Information Theory and Automation Czech Academy of Sciences

ISBN: (纸本)9781509018918

We propose a general data acquisition model with volatile random displacement of measured samples. Discrepancies between recorded and true positions of the original data is due to the nature of measured data or the acquisition device itself. A reconstruction method based on the Variational Bayesian inference is proposed, which estimates the original data from samples acquired with the acquisition model, and its relation to Jensen's inequality is discussed. A model variant of 2D image reconstruction is analyzed in detail. Further, we outline a relation between the proposed method and the classic deconvolution problem, and illustrate superiority of the Variational Bayesian approach in the case of small number of samples.

关键词： Bayesian inference RAW DATA image reconstruction reconstruction algorithm two dimentional images observational data Reconstructive Surgical Procedures TRUE POSITIONS

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Hyperspectral Image reconstruction Using a Deep Spatial-Spectral Prior

Hyperspectral Image Reconstruction Using a Deep Spatial-Spec...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Lizhi Wang Chen Sun Ying Fu Min H. Kim Hua Huang Beijing Institute of Technology Korea Advanced Institute of Science and Technology

ISBN: (纸本)9781728132945

Regularization is a fundamental technique to solve an ill-posed optimization problem robustly and is essential to reconstruct compressive hyperspectral images. Various handcrafted priors have been employed as a regularizer but are often insufficient to handle the wide variety of spectra of natural hyperspectral images, resulting in poor reconstruction quality. Moreover, the prior-regularized optimization requires manual tweaking of its weight parameters to achieve a balance between the spatial and spectral fidelity of result images. In this paper, we present a novel hyperspectral image reconstruction algorithm that substitutes the traditional hand-crafted prior with a data-driven prior, based on an optimization-inspired network. Our method consists of two main parts: First, we learn a novel data-driven prior that regularizes the optimization problem with a goal to boost the spatial-spectral fidelity. Our data-driven prior learns both local coherence and dynamic characteristics of natural hyperspectral images. Second, we combine our regularizer with an optimization-inspired network to overcome the heavy computation problem in the traditional iterative optimization methods. We learn the complete parameters in the network through end-to-end training, enabling robust performance with high accuracy. Extensive simulation and hardware experiments validate the superior performance of our method over the state-of-the-art methods.

关键词： hyperspectral imagery data driven image reconstruction network parameters reconstruction algorithm Robust control Optimization methods dynamic characteristics Reconstructive Surgical Procedures Fidelity

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Robust Signal-Structure reconstruction

Robust Signal-Structure Reconstruction

引用

IEEE Annual Conference on Decision and Control

作者： V. Chetty D. Hayden J. Goncalves S. Warnick Information and Decision Algorithms Laboratories Brigham Young University Control Group Department of Engineering University of Cambridge

ISBN: (纸本)9781467357159

This paper focuses on the reconstruction of the signal structure of a system in the presence of noise and nonlinearities. Previous results on polynomial time reconstruction in this area were restricted to systems where target specificity was part of the inherent structure, [5]. This work extends these results to all reconstructible systems and proposes a faster reconstruction algorithm along with an improved model selection procedure. Finally, a simulation study then details the performance of this new algorithm on reconstructible systems.

关键词： signal structure reconstruction algorithm model selection Polynomial time

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Deep Geometric Prior for Surface reconstruction

Deep Geometric Prior for Surface Reconstruction

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Francis Williams Teseo Schneider Claudio Silva Denis Zorin Joan Bruna Daniele Panozzo New York University

ISBN: (纸本)9781728132945

The reconstruction of a discrete surface from a point cloud is a fundamental geometry processing problem that has been studied for decades, with many methods developed. We propose the use of a deep neural network as a geometric prior for surface reconstruction. Specifically, we overfit a neural network representing a local chart parameterization to part of an input point cloud using the Wasserstein distance as a measure of approximation. By jointly fitting many such networks to overlapping parts of the point cloud, while enforcing a consistency condition, we compute a manifold atlas. By sampling this atlas, we can produce a dense reconstruction of the surface approximating the input cloud. The entire procedure does not require any training data or explicit regularization, yet, we show that it is able to perform remarkably well: not introducing typical overfit-ting artifacts, and approximating sharp features closely at the same time. We experimentally show that this geometric prior produces good results for both man-made objects containing sharp features and smoother organic objects, as well as noisy inputs. We compare our method with a number of well-known reconstruction methods on a standard surface reconstruction benchmark.

关键词： Surface reconstruction Clouds reconstruction algorithm Neural network Atlas atlas Saturn satellites Noise

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LOW RESOLUTION FACE RECOGNITION AND reconstruction VIA DEEP CANONICAL CORRELATION ANALYSIS

LOW RESOLUTION FACE RECOGNITION AND RECONSTRUCTION VIA DEEP ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhao Zhang Yun-Hao Yuan Xiao-Bo Shen Yun Li School of Information Engineering Yanzzhou University 225127 China School of Computer Science and Engineering Nanyang Technological University 639798 Singapoe

ISBN: (纸本)9781538646595

Low-resolution (LR) face identification is always a challenge in computer vision. In this paper, we propose a new LR face recognition and reconstruction method using deep canonical correlation analysis (DCCA). Unlike linear CCA-based methods, our proposed method can learn flexible nonlinear representations by passing LR and high-resolution (HR) image principal component features through multiple stacked layers of nonlinear transformation. As the nonlinear transformation in deep neural networks is implicit, we apply radial basis function based neural network to learn an explicit mapping between principal components and correlational features. In addition, we also design two residual compensation methods for identification and vision enhancement, respectively. The proposed approach is compared with existing LR face recognition and reconstruction algorithms. A number of experimental results on benchmark datasets have demonstrated the effectiveness and robustness of our method.

关键词： Low resolution face recognition image reconstruction deep learning canonical correlation Facial Recognition multivariate analysis image reconstruction Machine Learning Reconstructive Surgical Procedures reconstruction algorithm compensation method images Neural network

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REDUCING THE COMPUTATIONAL COMPLEXITY OF reconstruction IN COMPRESSED SENSING NONUNIFORM SAMPLING

REDUCING THE COMPUTATIONAL COMPLEXITY OF RECONSTRUCTION IN C...

引用

European Signal Processing Conference

作者： Ruben Grigoryan Tobias Lindstrom Jensen Thomas Arildsen Torben Larsen Department of Electronic Systems Aalborg University

This paper proposes a method that reduces the computational complexity of signal reconstruction in single-channel nonuniform sampling while acquiring frequency sparse multi-band signals. Generally, this compressed sensing based signal acquisition allows a decrease in the sampling rate of frequency sparse signals, but requires computationally expensive reconstruction algorithms. This can be an obstacle for real-time applications. The reduction of complexity is achieved by applying a multi-coset sampling procedure. This proposed method reduces the size of the dictionary matrix, the size of the measurement matrix and the number of iterations of the reconstruction algorithm in comparison to the direct single-channel approach. We consider an orthogonal matching pursuit reconstruction algorithm for single-channel sampling and its modification for multi-coset sampling. Theoretical as well as numerical analyses demonstrate order of magnitude reduction in execution time for typical problem sizes without degradation of the signal reconstruction quality.

关键词： Compressed sensing Multi-coset sampling Nonuniform sampling reconstruction algorithm

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Direct Signal's Impact on BISAR Signal Formation and Image reconstruction

Direct Signal's Impact on BISAR Signal Formation and Image R...

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International Radar Symposium

作者： Andon Lazarov Hristo Kabakchiev Todor Kostadinov Department of Informatics and Technical Sciences BFU Department of Software Technologies Sofia University "St. Clement of Ohrid" Department of Technical Sciences Burgas University As. Zlatarov

ISBN: (纸本)9781479978410

In this work the evaluation of the direct signal impact on the process of signal formation and image reconstruction, and on the quality of the reconstructed image in Bistatic Inverse Synthetic Aperture Radar (BISAR) is discussed Geometric description of BISAR topology with space-based transmitter and receiver, and moving target is given. Kinematic vector equations are derived. The composition of the BISAR pulse signal with linear frequency modulation (LFM) reflected from the object and direct signal from the transmitter is analytically described Image reconstruction algorithm is suggested. Numerical experiments to verify the composed signal model and imaging algorithm are carried out.

关键词： image reconstruction signals Mentoring Inverse synthetic aperture radar signal transmitter Transmitters reconstruction algorithm Chirp modulation vector equation pulse signal

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Iterative reconstruction for bioluminescence tomography based on an adaptive region shrinking strategy

Iterative reconstruction for bioluminescence tomography base...

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2015 IET International Conference on Biomedical Image and Signal Processing (ICBISP 2015)

作者： Hongbo Guo Xiaowei He Jingjing Yu Yuqing Hou Shuling Zhang Fang Dong Chen Jin School of Information Sciences and Technology Northwest University Xi'an 710069 China School of Physics and Information Technology Shaanxi Normal University Xi'an China

The main challenge of bioluminescence tomography (BLT) is the ill-posed, non-unique nature of the inverse problem. To get reliable reconstruction, permissible source region is a commonly used a priori knowledge in the inverse procedure. In this paper, to accurately reveal 3D distribution of bioluminescent sources from limited boundary measurement, we propose an iterative reconstruction method incorporating adaptive algebraic reconstruction technique (AART) and adaptively shrinking permissible source region. AART algorithm is applied to get the solution without permissible region. Base on the distribution of the solution, we calculate the expectation and the covariance matrix and then derive the parameters for determining a cuboid-shaped region. Simulation experiments on a 3D digital mouse and an in vivo experiment are conducted to validate the feasibility and evaluate the performance of the proposed reconstruction method. The reconstructed results demonstrate the shrinking strategy is helpful for improving the stability of inverse algorithm.

关键词： bioluminescence tomography finite element method inverse problems iterative reconstruction method Inverse method Finite element method Reconstructive Surgical Procedures shrinkage Bioluminescence variance covariance matrix inverse algorithms reconstruction algorithm Iterative Tomography reconstructions Region

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Image Super-resolution reconstruction Based on Bionic Intelligence

Image Super-resolution Reconstruction Based on Bionic Intell...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Xin Wang Qiong Wang Guofang Lv Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China College of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9781728123264

Traditional super-resolution reconstruction methods usually process a low resolution image as a whole, which ignores the differences among image patches, resulting in ineffective reconstruction result. In this paper, we adopt the concept of patch as the basic unit of super resolution reconstruction, and establish a novel image super-resolution reconstruction method based on bionic intelligence. The original low resolution image is initially reconstructed by bicubic interpolation. Then, a biological visual attention mechanism inspired approach is applied to the initial reconstruction result to detect the salient regions. Subsequently, a super-resolution convolutional neural network model is applied to the salient image patches so as to get the precise reconstruction results. Extensive experiments manifest that the proposed method outperforms many current state-of-the-art schemes in reconstruction quality, and achieves fast speed for practical application.

关键词： Super-resolution Bionic intelligence Saliency detection Convolutional neural network super-resolution Reconstructive Surgical Procedures image patch bionics reconstruction algorithm Low-resolution image image resolution reconstructions Intelligence

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