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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data III"

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Anisotropic diffusion of surface normals for feature preserving surface reconstruction

Anisotropic diffusion of surface normals for feature preserv...

引用

International conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： T. Tasdizen R. Whitaker School of Computing University of Utah USA

For 3D surface reconstruction problems with noisy and incomplete range data measured from complex scenes with arbitrary topologies, a low-level representation, such as level set surfaces, is used. Such surface reconstruction is typically accomplished by minimizing a weighted sum of datamodel discrepancy and model smoothness terms. We introduce a new nonlinear model smoothness term for surface reconstruction based on variations of the surface normals. A direct solution requires solving a fourth-order partial differential equation (PDE), which is very difficult with; conventional numerical techniques. Our solution is based on processing the normals separately from the surface, which allows us to separate the problem into two second-order PDEs. The proposed method can smooth complex, noisy surfaces, while preserving sharp, geometric features, and it is a natural generalization of edge-preserving methods in image processing, such as anisotropic diffusion.

关键词： Surface reconstruction Anisotropic magnetoresistance image reconstruction Layout Noise measurement Topology Level set Shape Density measurement Multi-stage noise shaping

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Conditional entropy maximization for PET

Conditional entropy maximization for PET

引用

IEEE International conference on Systems, Man and Cybernetics

作者： P.P. Mondal K. Rajan Department of Physics Indian Institute of Science Bangalore India

Maximum Likelihood (ML) estimation is extensively used for estimating emission densities from clumped and incomplete measurement data in Positron Emission Tomography (PET) modality. reconstruction produced by ML-algorithm has been found noisy because it does not make use of available prior knowledge. Bayesian estimation provides such a platform for the inclusion of prior knowledge in the reconstruction procedure. But modeling a prior distribution is a cumbersome task and needs a lot of insight. In this work, we have proposed a conditional entropy maximization algorithm for PET modality, which is a generalization of maximum likelihood algorithm. We have imposed self-normalization condition for the determination of prior distribution. It is found that as prior distribution tends to uniform distribution, the conditional entropy maximization algorithm reduces to maximum likelihood algorithm. Simulated experimental results have shown that images reconstructed using maximum entropy algorithm are qualitatively better than those generated by ML-algorithm.

关键词： Entropy Positron emission tomography Maximum likelihood estimation image reconstruction Bayesian methods Parameter estimation Physics Distribution functions Probability density function Density measurement

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Imaging fluorescence parameters by Bayesian optical diffusion tomography

Imaging fluorescence parameters by Bayesian optical diffusio...

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2nd conference on image reconstruction from incomplete data

作者： Millane, RP Milstein, AM Zhang, Q Oh, S Webb, KJ Bouman, CA Boas, DA Univ Canterbury Dept Elect & Comp Engn Christchurch New Zealand

ISBN: (纸本)0819445592

A Bayesian optimization scheme is presented for reconstructing fluorescent yield and lifetime, the absorption coefficient, and-the scattering coefficient in turbid media, such as biological tissue. The method utilizes measurements at both the excitation and emission wavelengths for reconstructing all unknown parameters. The effectiveness of the reconstruction algorithm is demonstrated by simulation and by application to experimental data from a tissue phantom containing a fluorescent agent.

关键词： optical diffusion tomography fluorescence imaging Bayesian image reconstruction inverse problem nonlinear optimization

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Complex-valued image reconstruction from spectral phase or magnitude

Complex-valued image reconstruction from spectral phase or m...

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2nd conference on image reconstruction from incomplete data

作者： Tang, XQ Porat, M Univ Auckland Dept Elect & Elect Engn Auckland 1 New Zealand

ISBN: (纸本)0819445592

Many significant features of images are represented in their Fourier transform. The spectral phase of an image can often be measured more precisely than magnitude for frequencies of up to a few GHz. However, spectral magnitude is the only measurable data in many imaging applications. In this paper, the reconstruction of complex-valued images from either the phases or magnitudes of their Fourier transform is addressed. Conditions for unique representation of a complex-valued image by its spectral magnitude combined with additional spatial information is investigated and presented. reconstruction algorithms of complex-valued images are developed and introduced. Three types of reconstruction algorithms are presented. (1) Algorithms that reconstruct a complex-valued image from the magnitude of its discrete Fourier transform and part of its spatial samples based on the autocorrelation function. (2) Iterative algorithms based on the Gerchberg and Saxton approach. (3) Algorithms that reconstruct a complex-valued image from its localized Fourier transform magnitude. The advantages of the proposed algorithms over the presently available approaches are presented and discussed.

关键词： complex-valued image unique representation image reconstruction Fourier magnitude Fourier phase

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Geometric Bayesian inpainting and applications

Geometric Bayesian inpainting and applications

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2nd conference on image reconstruction from incomplete data

作者： Shen, JH Univ Minnesota Sch Math Computat & Appl Math Minneapolis MN 55455 USA

ISBN: (纸本)0819445592

Inpainting is an image interpolation problem, with broad applications in image processing andthe digital technology. This paper presents our recent efforts in developing inpainting models based on the Bayesian and variational principles. We discuss several geometric image (prior) models, their role in the construction of variational inpainting models, the resulting Euler-Lagrange differential equations, and their numerical implementation.

关键词： inpainting interpolation Bayesian image model Euler's elastica computational PDE

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Proceedings of SPIE: image reconstruction from incomplete data II

Proceedings of SPIE: Image reconstruction from incomplete da...

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image reconstruction from incomplete data II

The proceedings contains 22 papers from the SPIE conference on image reconstruction from incomplete data II. Topics discussed include: comparison of reconstruction algorithms for images from sparse-aperture systems;imaging fluorescence parameters by Bayesian optical diffusion tomography;blind deconvolution of speckle images constrained by wavefront sensing data;automated target morphing applied to objects in cluttered backgrounds;reconstruction of seismic data using adaptive regularization;and continuous and discrete space particle filters for predictions in acoustic positioning.

关键词： image reconstruction

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reconstruction of seismic data using adaptive regularization

Reconstruction of seismic data using adaptive regularization

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2nd conference on image reconstruction from incomplete data

作者： Liu, B Sacchi, MD Univ Alberta Dept Phys Edmonton AB T6G 2J1 Canada

ISBN: (纸本)0819445592

In seismic data processing, we often need to interpolate/extrapolate missing spatial locations in a domain of interest. The reconstruction problem can be posed as an inverse problem where from inadequate and incomplete data one attempts to recover the complete band-limited seismic wavefield. However, the problem is often ill posed due to factors such as inaccurate knowledge of bandwidth and noise. In this case, regularization can be used to help to obtain a unique and stable solution. In this paper, we formulate band-limited data reconstruction as a minimum norm least squares type problem where an adaptive DFT-weighted norm regularization term is used to constrain the solution. In particular, the regularization term is iteratively updated through using the modified periodogram of the estimated data. The technique allows for adaptive incorporation of prior knowledge of the data such as the spectrum support and the shape of the spectrum. The adaptive regularization can be accelerated using FFTs and an iterative solver like preconditioned conjugate gradient algorithm. Examples on synthetic and real seismic data illustrate improvement of the new method over damped least squares estimation.

关键词： band-limited signal reconstruction DFT-weighted norm regularization FFT preconditioning

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Comparison of reconstruction algorithms for images from sparse-aperture systems

Comparison of reconstruction algorithms for images from spar...

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2nd conference on image reconstruction from incomplete data

作者： Fienup, JR Griffith, D Harrington, L Kowalczyk, AM Miller, JJ Mooney, JA Univ Rochester Inst Opt Rochester NY 14627 USA

ISBN: (纸本)0819445592

Telescopes and imaging interferometers with sparsely filled apertures can be lighter weight and less expensive than conventional filled-aperture telescopes. However, their greatly reduced MTF's cause significant blurring and loss of contrast in the collected imagery. image reconstruction algorithms can correct the blurring completely when the signal-to-noise ratio (SNR) is high, but only partially when the SNR is low. This paper compares both linear (Wiener) and nonlinear (iterative maximum likelihood) algorithms for image reconstruction under a variety of circumstances. These include high and low SNR, Gaussian noise and Poisson-noise dominated, and a variety of aperture configurations and degrees of sparsity. The quality metric employed to compare algorithms is image utility as quantified by, the National imagery Interpretability Rating Scale (NIIRS). On balance, a linear reconstruction algorithm with a power-law power-spectrum estimate performed best.

关键词： image reconstruction sparse apertures deconvolution image restoration MTF image quality Wiener filter power spectrum estimation maximum likelihood

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Three-dimensional surface approximation from incomplete data using distance transform

Three-dimensional surface approximation from incomplete data...

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2nd conference on image reconstruction from incomplete data

作者： Lee, DJ Westover, B Eifert, J Brigham Young Univ Dept Elect & Comp Engn Provo UT 84602 USA

ISBN: (纸本)0819445592

Three-dimensional (3-D) object surface reconstruction is an important step toward non-destructive measurements of surface area and volume. Laser triangulation technique has been widely used for obtaining 3-D information. However, the 3-D data obtained from triangulation are not dense enough or usually not complete for surface reconstruction, especially for objects with irregular shape. As a result of fitting surfaces with these sparse 3-D data, inaccuracy in measuring the surface area or calculating the volume of the object is inevitable. A computer vision technique combining laser triangulation and distance transform has been developed to improve the measurement accuracy for objects with irregular shape. A 3-D wire-frame model is generated first with all available 3-D data. Each pixel within the image boundary is given the distance information using distance transform. The distance information of each pixel is then used as the constraints for surface fitting and interpolation. With this additional information from distance transform, more accurate surface approximation can be achieved. The measurement accuracy of this technique is compared with other interpolation techniques for the volume measurement of oyster meats.

关键词： distance transform laser triangulation 3-D wire-frame model machine vision surface fitting surface interpolation oyster

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Blind deconvolution of speckle images constrained by wavefront sensing data

Blind deconvolution of speckle images constrained by wavefro...

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2nd conference on image reconstruction from incomplete data

作者： Leung, WYV Clare, RM Lane, RG Univ Canterbury Dept Elect & Comp Engn Christchurch New Zealand

ISBN: (纸本)0819445592

The predominant effect of the atmosphere on the incoming wavefront of an astronomical object is the introduction of a phase distortion, resulting in,a speckle image at the ground-based telescope. Deconvolution from wavefront sensing is an imaging technique used to compensate for the degradation due to atmospheric turbulence, where the point spread function is estimated from the wavefront sensing data. However in this approach any information in the speckle images regarding the point spread function is not utilised. This paper investigates the joint application of wavefront sensing data and speckle images in reconstructing the point spread function and the object in a Bayesian framework. The results on experimental data demonstrate the feasibility of this approach even under very low light levels.

关键词： blind deconvolution wavefront sensing Shack-Hartmann sensor

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