Image reconstruction from TE scattering data using equation ofstrong permittivity fluctuation

Compared to the TM case, the inverse scattering problem for the TE incident field is more complicated due to its stronger nonlinearity. This work provides an effective method for the reconstruction of two-dimensional (2-D) inhomogeneous dielectric objects from TE scattering data. The algorithm applies the distorted Born iterative method to the integral equation of strong permittivity fluctuation to reconstruct scatterers with high-permittivity contrast. Numerical simulations are performed and the results show that the distorted Born iterative method (DBIM) for strong permittivity fluctuation (SPF-DBIM) converges faster and can obtain better reconstructions for objects with larger dimensions and higher contrasts in comparison with ordinary DBIM. A frequency hopping technique is also applied to further increase the contrast     

Image reconstruction from the 1997 Ipswich data using aconjugate-gradient algorithm

The conjugate gradient algorithm is shown to be effective in reconstructing targets from experimental radar measurements. Enhancement, using the reciprocity relation to complete the scattered data, is also shown. The small number of iterations needed to obtain a satisfying solution (less than 50 iterations), combined with the increasing computation power of new processors (an SGI R10000 needs 30 sec to compute one iteration), make it possible for this algorithm to be used for practical applications     

On Bayesian image reconstruction from projections: uniform and nonuniform a priori source information

A method that incorporates a priori uniform or nonuniform source distribution probabilistic information and data fluctuations of a Poisson nature is presented. The source distributions are modeled in terms of a priori source probability density functions. Maximum a posteriori probability solutions, as determined by a system of equations, are given. Interactive Bayesian imaging algorithms for the solutions are derived using an expectation maximization technique. Comparisons of the a priori uniform and nonuniform Bayesian algorithms to the maximum-likelihood algorithm are carried out using computer-generated noise-free and Poisson randomized projections. Improvement in image reconstruction from projections with the Bayesian algorithm is demonstrated. Superior results are obtained using the a priori nonuniform source distribution.     

Image reconstruction from frequency-offset Fourier data

Motivated by the ability of synthetic-aperture radar and related imaging systems to produce images of surprisingly high quality, we consider the problem of reconstructing the magnitude of a complex signal f from samples of the Fourier transform of f located in a small region offset from the origin. It is shown that high-quality speckle reconstructions are possible so long as the phase of f is highly random. In this case, the quality of the reconstruction is insensitive to the location of the known Fourier data, and edges at all orientations are reproduced equally well. A large number of computer examples are presented demonstrating these attributes. Methods for improving image quality are also briefly discussed.     

Minimum mean squared error equalization using a priori information

A number of important advances have been made in the area of joint equalization and decoding of data transmitted over intersymbol interference (ISI) channels. Turbo equalization is an iterative approach to this problem, in which a maximum a posteriori probability (MAP) equalizer and a MAP decoder exchange soft information in the form of prior probabilities over the transmitted symbols. A number of reduced-complexity methods for turbo equalization have been introduced in which MAP equalization is replaced with suboptimal, low-complexity approaches. We explore a number of low-complexity soft-input/soft-output (SISO) equalization algorithms based on the minimum mean square error (MMSE) criterion. This includes the extension of existing approaches to general signal constellations and the derivation of a novel approach requiring less complexity than the MMSE-optimal solution. All approaches are qualitatively analyzed by observing the mean-square error averaged over a sequence of equalized data. We show that for the turbo equalization application, the MMSE-based SISO equalizers perform well compared with a MAP equalizer while providing a tremendous complexity reduction     

Bayesian reconstruction and use of anatomical a priori information for emission tomography

A Bayesian method is presented for simultaneously segmenting and reconstructing emission computed tomography (ECT) images and for incorporating high-resolution, anatomical information into those reconstructions. The anatomical information is often available from other imaging modalities such as computed tomography (CT) or magnetic resonance imaging (MRI). The Bayesian procedure models the ECT radiopharmaceutical distribution as consisting of regions, such that radiopharmaceutical activity is similar throughout each region. It estimates the number of regions, the mean activity of each region, and the region classification and mean activity of each voxel. Anatomical information is incorporated by assigning higher prior probabilities to ECT segmentations in which each ECT region stays within a single anatomical region. This approach is effective because anatomical tissue type often strongly influences radiopharmaceutical uptake. The Bayesian procedure is evaluated using physically acquired single-photon emission computed tomography (SPECT) projection data and MRI for the three-dimensional (3-D) Hoffman brain phantom. A clinically realistic count level is used. A cold lesion within the brain phantom is created during the SPECT scan but not during the MRI to demonstrate that the estimation procedure can detect ECT structure that is not present anatomically.     

Image reconstruction from Ipswich data. II

In this paper, we describe the results obtained by using variants of the modified gradient method. These are applied to the new Ipswich data sets to reconstruct the shape, location, and/or index of refraction of unknown two-dimensional scatterers (infinite cylinders in three dimensions)     

Guiding ziplock snakes with a priori information

In this paper, we present a method to combine a grammatical model that encodes a priori shape information with the ziplock snakes presented by Neuenschwander et al. (1997). A competing mechanism is adopted to take advantage of the shape models without inducing excessive computation. The resulting model-based ziplock snakes have many advantages over the original model: they can accurately locate contour features, produce more refined results, and deal with multiple contours, missing image cues, and noise.     

Image reconstruction from multiple frames of sparse data

High-resolution image reconstruction from sparse data is an important problem in sensor array imaging (SAI). The reconstructed images from such a data are poorly resolved. However, there may exist possibilities of making multiple measurements, for example, collecting many frames of data in a dynamic scene situation where there is relative motion between the object and the receiver. We discuss here a method of reconstructing good-quality images from multiple frames of sparse data obtained from a simulated dynamic scene situation. This method, based on projection onto convex sets (POCS), not only restricts the solution set by satisfying the constraints in the multiple measurements but also reconstructs a high-resolution image.     

Image reconstruction from acoustical holograms using microprocessors

An acoustic imaging system using holographic techniques is described. Insonification of the object is achieved using a phased array scanner. Image reconstruction uses Fourier Transformations to produce a 128 × 128 pixel TV display. The system has the ability to focus in the ‘near field’ of its transducer arrays and has a frame rate of approximately 0.3 Hz.     

Imaging using limited-angle backscattered data from real targets

We discussed a spectral-estimation method, referred to as the PDFT, which was used to overcome the problems of limited k-space data being measured from a monostatic or backscatter radar system. We have also shown how this estimation method can be used to estimate the size of a target, and also how the method permits target recognition to be achieved with only a small amount of backscatter data. That amount of required data points decreases as the information about possible target shapes, expressed by the prior estimate, gets more and more specific     

Using a priori information to improve soil moisture retrieval from ENVISAT ASAR AP data in semiarid regions

This paper presents a retrieval algorithm that estimates spatial and temporal distribution of volumetric soil moisture content, at an approximate depth of 5 cm, using multitemporal ENVISAT Advanced Synthetic Aperture Radar (ASAR) alternating polarization images, acquired at low incidence angles (i.e., from 15/spl deg/ to 31/spl deg/). The algorithm appropriately assimilates a priori information on soil moisture content and surface roughness in order to constrain the inversion of theoretical direct models, such as the integral equation method model and the geometric optics model. The a priori information on soil moisture content is obtained through simple lumped water balance models, whereas that on soil roughness is derived by means of an empirical approach. To update prior estimates of surface parameters, when no reliable a priori information is available, a technique based solely on the use of multitemporal SAR information is proposed. The developed retrieval algorithm is assessed on the Matera site (Italy) where multitemporal ground and ASAR data were simultaneously acquired in 2003. Simulated and experimental results indicate the possibility of attaining an accuracy of approximately 5% in the retrieved volumetric soil moisture content, provided that sufficiently accurate a priori information on surface parameters (i.e., within 20% of their whole variability range) is available. As an example, multitemporal soil moisture maps at watershed scale, characterized by a spatial resolution of approximately 150 m, are derived and illustrated in the paper.     

Image reconstruction from incomplete projection data: iterative reconstruction-reprojection techniques

This paper addresses the task of image reconstruction from an incomplete set of projection data. Several methods which first estimate the missing data and then utilize standard reconstruction algorithms to obtain an image are investigated. Results from simulations are presented which illustrate the difficulty in comparing algorithms objectively, particularly when a simple test phantom is chosen. The incorporation of a priori information into the algorithm, an approach which has previously been discussed in the literature, is shown to produce faster convergence.     

Image reconstruction by deconvolution using a wavefront sensor

Conditions for reconstructing a distorted image by the deconvolution from a phase distribution over the aperture, detected by a wavefront sensor, are studied.     

Microwave imaging-Location and shape reconstruction frommultifrequency scattering data

The problem of determining the shape and location of an object embedded in a homogeneous dissipative medium from measurements of the field scattered by the object is considered in this paper. The object is assumed to be an infinite cylinder of known cross section illuminated by a TM plane wave and the scattered field is measured on a line segment perpendicular to the direction of incidence. Measurement data are carried out at three different frequencies for a homogeneous cylinder of known dielectric constant. The location and contour shape are determined using two different reconstruction algorithms, a Newton-Kantorovich (NK) method and the modified gradient (MG) method whose effectiveness and robustness are compared. Both methods are based on domain integral representations of the field in the body. They involve an iterative minimization of the defect between an integral representation of the field measured on the line and the actual measured data. The NK method involves a linearization of the nonlinear relation between the field and the contrast, as well as the solution of a direct scattering problem at each iteration. The MG method seeks the simultaneous reconstruction of the field and the characteristic function of the support of the scatterer without solving a direct problem at each step. Both methods employed the same initial guess and the a priori information that the characteristic function is nonnegative     

Blind convolution using signal reconstruction from partial higherorder cepstral information

A blind deconvolution scheme for the reconstruction of a signal that propagates in the presence of reverberation and additive noise is presented. The reconstruction employs data collected by two receivers placed well apart from each other. Each received sequence consists of the convolution of the transmitted signal with a channel. Applying the bicepstrum iterative reconstruction algorithm on the differences of the cepstra coefficients of the two received sequences, the cepstra coefficients of the transmission channels can be computed and used for the reconstruction of the transmitted signal. The deconvolution can be performed even if the data sequences are corrupted by additive noise. The computation of the cepstra coefficients is based on the cross-bispectrum if the noise processes present in the observation sequences are zero mean and uncorrelated, while the bicepstrum of each observation is used if the noise processes are zero mean correlated with a symmetric probability density function     

Image upconversion using coherent anti-stokes Raman scattering

We report an experimental and theoretical study of real-time image upconversion with coherent anti-Stokes Raman scattering. Experimental results for image upconversion in hydrogen are presented. Various parameters affecting the efficiency of the upconverted signal and image resolution are discussed     

Image reconstruction from localized phase

The authors present a novel approach to image representation using partial information defined by the localized phase. The scheme is implemented using the short-time (short-distance) Fourier transform. This is a generalization of the Gabor scheme which is well-established with regard to biological representation of visual information at the level of the visual cortex. Similar to processing in vision, the DC component is first extracted from the signal and treated separately. Computational results and theoretical analysis indicate that image reconstruction from the localized phase representation requires fewer computer operations and yields an improved rate of convergence compared to reconstruction from the global phase representation. It is also implementable with fast algorithms using highly parallel architecture