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

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reconstruction of seismic data using adaptive regularization

Reconstruction of seismic data using adaptive regularization

引用

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...

引用

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...

引用

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...

引用

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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Sampling, radiometry, and image reconstruction for polar and geostationary meteorological remote sensing systems

Sampling, radiometry, and image reconstruction for polar and...

引用

2nd conference on image reconstruction from incomplete data

作者： Abrams, MC Cain, SC ITT Industries Inc. (United States) Indiana Univ.-Purdue Univ./Fort Wayne (United States)

ISBN: (纸本)0819445592

In this paper, a Bayesian-based image reconstruction scheme is utilized for estimating a high resolution temperature map of the top of the earth's atmosphere using the GOES-8 (Geostationary Operational Environmental Satellite) imager infrared channels. By simultaneously interpolating the image while estimating temperature, the proposed algorithm achieves a more accurate estimate of the sub-pixel temperatures than could be obtained by performing these operations independently of one another. The proposed algorithm differs from other Bayesian-based image interpolation schemes in that it estimates brightness temperature as opposed to image intensity and incorporates a detailed optical model of the GOES multi-channel imaging system. The temperature estimation scheme is compared to deconvolution via pseudo-inverse filtering using two metrics. One metric is the mean squared temperature error. This metric describes the radiometric accuracy of the image estimate. The second metric is the recovered Modulation Transfer Function (MTF) of the image estimate. This method has traditionally been used to evaluate the quality of image recovery techniques. It will be shown in this paper that there is an inconsistency between these two metrics in that an image with high spatial frequency content can be reconstructed with poor radiometric, accuracy. The ramifications of this are discussed in order to evaluate the two metrics for use in quantifying the performance of image reconstruction algorithms.

关键词： Imaging systems image restoration reconstruction algorithms Sensors image resolution Modulation transfer functions Radiometry data modeling Satellite imaging Satellites

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Super-resolution in image restoration of wide area images viewed through atmospheric turbulence

Super-resolution in image restoration of wide area images vi...

引用

2nd conference on image reconstruction from incomplete data

作者： Lambert, A Fraser, D Jahromi, MRS Hunt, BR Univ New S Wales Univ Coll Kensington NSW 2033 Australia

ISBN: (纸本)0819445592

We postulate that under anisoplanatic imaging conditions involving imaging through turbulent media over a wide-area there exists the possibility of spatial frequency content that is normally lost outside the aperture of an imaging instrument under unperturbed viewing conditions, being aliased into the aperture. Simulation is presented that reinforces this premise. We apply restoration algorithms that were designed to correct non-uniform distortions, to a real image sequence to the effect of noticing the de-aliased super-frequency content. We claim this to be super-resolution, and that it is only possible under anisoplanatic imaging scenarios, where the point spread function of the image is position dependent as a result of the atmospheric turbulence.

关键词： wide-area image restoration super-resolution surveillance imaging astronomical imaging

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Fast segmentation method for SAR images

Fast segmentation method for SAR images

引用

2nd conference on image reconstruction from incomplete data

作者： Bratsolis, E Sigelle, M Ecole Natl Super Telecommun Bretagne Dept Traitement Signal & Images F-75013 Paris France

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).

关键词： image segmentation speckle reduction restoration Markov random field (MRF) synchronous local iterative method (SLIM) synthetic aperture radar (SAR) total sum preserving regularization (TSPR)

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Deconvolution in line scanners using a-priori information

Deconvolution in line scanners using a-priori information

引用

2nd conference on image reconstruction from incomplete data

作者： Wirnitzer, B Spraggon-Hernandez, T Univ Appl Sci Mannheim D-68163 Mannheim Germany

ISBN: (纸本)0819445592

In a digital camera the MTF of the optical system must comprise a low-pass filter in order to avoid aliasing. The MTF of incoherent imaging usually and in principle is far from an ideal low-pass. Theoretically a digital ARMA-Filter can be used to compensate for this drawback. In praxis such deconvolution filters suffer from instability because of time-variant noise and space-variance of the MTF. In addition in a line scanner the MTF in scan direction slightly differs in each scanned image. Therefore inverse filtering will not operate satisfactory in an unknown environment. A new concept is presented which solves both problems using a-priori information about an object, e.g. that parts of it are known to be binary. This information is enough to achieve a stable space and time-variant ARMA-deconvolution filter. Best results are achieved using non linear filtering and pattern feedback. The new method was used to improve the bit-error-rate (BER) of a high-density matrix-code scanner by more than one order of magnitude. An audio scanner will be demonstrated, which reads 12 seconds of music in CD-quality from an audio coded image of 18mmx55mm size.

关键词： image deconvolution decision feedback non-linear filter two dimensional adaptive filter

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Multi-frame blind deconvolution and bispectrum processing of atmospherically-degraded data: a comparison

Multi-frame blind deconvolution and bispectrum processing of...

引用

2nd conference on image reconstruction from incomplete data

作者： Matson, CL Schulze, KJ Billings, P Tyler, DW Directed Energy Directorate/Air Force Research Lab. (United States) KJS Consulting (United States) Textron Systems Corp. (United States) Univ. of New Mexico (United States)

ISBN: (纸本)0819445592

We analyze the quality of reconstructions obtained when using the multi-frame blind deconvolution (MFBD) algorithm and the bispectrum algorithm to reconstruct images from atmospherically-degraded data that are corrupted by detector noise. In particular, the quality of reconstructions is analyzed in terms of the fidelity of the estimated Fourier phase spectra. Both the biases and the mean square phase errors of the Fourier spectra estimates are calculated and analyzed. The reason that the comparison is made by looking at the Fourier phase spectra is because both the MFBD and bispectrum algorithms can estimate Fourier phase information from the image data itself without requiring knowledge of the system transfer function, and because Fourier phase plays a dominant role in image quality. Computer-simulated data is used for the comparison in order to be able to calculate true biases and mean square errors in the estimated Fourier phase spectra. For the parameters in this study, the bispectrum algorithm produced less-biased phase estimates in all cases than the MFBD algorithm. The MFBD algorithm produced mean square phase errors comparable to or lower than the bispectrum algorithm for good seeing and few data frames, while the converse is true for many data frames and poor seeing.

关键词： speckle imaging multi-frame blind deconvolution MFBD bispectrum

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Inverse scattering of strongly scattering targets using redundant data sets

Inverse scattering of strongly scattering targets using redu...

引用

2nd conference on image reconstruction from incomplete data

作者： Semichaevsky, AV Ghogomu, P Al Ababneh, N Testorf, ME Fiddy, MA Univ Massachusetts Lowell Dept Elect & Comp Engn Lowell MA 01854 USA

ISBN: (纸本)0819445592

For weakly scattering permittivities, each measurement of the scattered far field can be interpreted as a sampling point of the Fourier transformation of the object. Furthermore, each sampling point can be accessed by more than one combination of wavelength, propagation direction, and polarization of the incident field. This means, a set of measurements which access the same sampling point can be regarded as being redundant. For strongly scattering objects the Fourier diffraction slice theorem does not apply. We show that measurements which are redundant in the weakly scattering case can be exploited to resolve difficulties associated with imaging of the strongly scattering objects. One dimensional geometries are investigated to estimate the potential redundant data sets offer for addressing the inverse scattering problem of strongly and multiply scattering objects. In addition, we discuss preliminary results for solving 2D imaging problems.

关键词： nonlinear inverse scattering strongly scattering objects multiple scattering polarimetric information diffraction tomography synthetic aperture radar

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