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...
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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 datafrom a tissue phantom containing a fluorescent agent.
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 var...
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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.
With the development of computed tomography (CT) technology, how to reconstruct high-quality pathological images under low-dose X-ray radiation has become the focus of attention. Compressed sensing theory has become a...
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Multi-objective optimization reconstruction algorithm is one of the main methods to solve the limited angle imagereconstruction. However we have great difficulty in selecting the right parameter reasonably because th...
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ISBN:
(纸本)9781424451944
Multi-objective optimization reconstruction algorithm is one of the main methods to solve the limited angle imagereconstruction. However we have great difficulty in selecting the right parameter reasonably because there is not only one objective function. So we give a new method to choose the parameter which was called homotopy parameter based on the theory of homotopy in this paper. Finally, the simulations were carried out to verify the proposed strategy, and the simulation results show that this algorithm is superior to the conventional multi-objective optimization methods in many aspects, such as less reconstruction errors, higher smoothness and gray value resolution.
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 incomplet...
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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 andincompletedata 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 datareconstruction 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.
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 ...
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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 imagefrom 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 imagefrom its localized Fourier transform magnitude. The advantages of the proposed algorithms over the presently available approaches are presented and discussed.
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 wa...
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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.
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 b...
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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. imagereconstruction 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 imagereconstruction 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.
Single-photon emission computed tomography (SPECT) is a method of choice for imaging spatial distributions of radioisotopes. Applications of this method are found in medicine, biomedical research and nuclear industry....
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Single-photon emission computed tomography (SPECT) is a method of choice for imaging spatial distributions of radioisotopes. Applications of this method are found in medicine, biomedical research and nuclear industry. This paper deals with improving spatial resolution in SPECT by applying correction for the point-spread function (PSF) in the reconstruction algorithm and optimizing the collimator. Several approaches are considered: the use of a depth-dependent PSF model for a parallel-beam collimator derived from experimental data, the extension of this model to a fan-beam collimator, a triangular approximation of the PSF for reconstruction acceleration, and a method for optimal fan-beam collimator design. An unmatched projector/backprojector ordered subsets expectation maximization (OSEM) algorithm is used for imagereconstruction. Experimental results with simulated and physical phantom data of a micro-SPECT system show a significant improvement of spatial resolution with the proposed methods.
An iterative optimization algorithm which can be used for speckle reduction and segmentation of synthetic aperture radar (SAR) images is presented here. This method contains as a first step a fast restoration and as a...
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ISBN:
(纸本)0819445592
An iterative optimization algorithm which can be used for speckle reduction and segmentation of synthetic aperture radar (SAR) images is presented here. This method contains as a first step a fast restoration and as a second one the segmentation. We have worked in 3-look simulated and real ERS-1 amplitude images. The iterative filter is based on a membrane model Markov random field (MRF) approximation optimized by a synchronous local iterative method (SLIM). The final form of restoration gives a total sum preserving regularization (TSPR).
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