Bobby R. Hunt

Matrix formulation of the reconstruction of phase values from phase differences

Methods in speckle imaging and adapative optics, as well as a new technique in digital image restoration, require the calculation of the Fourier phase spectrum from measurements of the differences on a two-dimensional grid of the phase spectrum. The calculation of phases from phase differences has been analyzed in the literature and relaxation mechanisms for computing the phase have been derived by least-squares analysis. In the following paper we formulate the phase reconstruction problem in terms of a vector-matrix multiplication, and we then show that previous solution methods are equivalent to this general description. We also analyze the errors in reconstruction and reconcile previously published error results based on simulations with an analytical error expression derived from Parseval’s theorem. Finally, we comment upon the rate of convergence of phase reconstructions, and discuss numerical analysis literature which indicates that the methods previously published for phase reconstruction can be made to converge much faster.

Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT

Two systems are presented for compression of hyperspectral imagery which utilize trellis coded quantization (TCQ). Specifically, the first system uses TCQ to encode transform coefficients resulting from the application of an 8/spl times/8/spl times/8 discrete cosine transform (DCT). The second systems uses DPCM to spectrally decorrelate the data, while a 2D DCT coding scheme is used for spatial decorrelation. Side information and rate allocation strategies are discussed. Entropy-constrained code-books are designed using a modified version of the generalized Lloyd algorithm. These entropy constrained systems achieve compression ratios of greater than 70:1 with average PSNRs of the coded hyperspectral sequences exceeding 40.0 dB. >

Signature verification using global and grid features

Abstract In this work, algorithms for extracting global geometric and local grid features of signature images were developed. These features were combined to build a multi-scale verification function. This multi-scale verification function was evaluated using statistical procedures. Results indicated that the multi-scale verification function yielded a lower verification error rate and higher reliability than the single-scale verification function using either global geometric or local grid feature representation. The correct verification rate of the multi-scale system was more than 90% in rejecting skilled forgeries and was perfect in rejecting simple forgeries based on a limited database.

Super‐resolution of images: Algorithms, principles, performance

A new facet of image restoration research has begun to emerge in recent years: super‐resolution of images, which we define as the processing of an image so as to recover object information from beyond the spatial frequency bandwidth of the optical system that formed the image. Simple Fourier analysis would indicate that super‐resolution is not possible. Therefore, it is important to reconcile this simplistic view with the existing algorithms that have been demonstrated to achieve super‐resolution. In this article, we consider some of the algorithms that have demonstrated super‐resolution and discuss the common principles that they share which makes it possible for them to recover some of the lost bandwidth of the object. We also consider the question of super‐resolution performance, which is the measure of how much lost bandwidth can be recovered from a super‐resolution algorithm, and how the performance is related to the algorithm principles that allow super‐resolution to occur. We conclude with examples of super‐resolution.

Voiced-unvoiced-silence classifications of speech using hybrid features and a network classifier

Voiced-unvoiced-silence classification of speech was done using a multilayer feedforward network. The network performance was evaluated and compared to that of a maximum-likelihood classifier. Results indicated that the network performance was not significantly affected by the size of the training set and a classification rate as high as 96% was obtained. >

Analysis of the limit to superresolution in incoherent imaging

Superresolution algorithms have demonstrated impressive image-restoration results in the space domain. We consider the limits on superresolution performance in terms of usable bandwidth of the restored frequency spectrum. On the basis of a characterization of the spectral extrapolation errors (viz., null objects), we derive an expression for an approximate bound on accurate bandwidth extension for the general class of superresolution algorithms that incorporate a priori assumptions of a nonnegative, space-limited object. It is shown that accurate bandwidth extension is inversely related to the spatial extent of the object and the noise level in the image. For superresolution of sampled data, we present preliminary results relating bandwidth extrapolation to the difference between sampling rate and the discrete optical cutoff frequency. Simulation results are presented that substantiate the derived bandwidth extrapolation bounds.

Iterative multiframe superresolution algorithms for atmospheric-turbulence-degraded imagery

The subject of interest is the superresolution of atmospheric-turbulence-degraded, short-exposure imagery, where superresolution refers to the removal of blur caused by a diffraction-limited optical system along with recovery of some object spatial-frequency components outside the optical passband. Photon-limited space object images are of particular interest. Two strategies based on multiple exposures are explored. The first is known as deconvolution from wave-front sensing, where estimates of the optical transfer function (OTF) associated with each exposure are derived from wave-front-sensor data. New multiframe superresolution algorithms are presented that are based on Bayesian maximum a posteriori and maximum-likelihood formulations. The second strategy is known as blind deconvolution, in which the OTF associated with each frame is unknown and must be estimated. A new multiframe blind deconvolution algorithm is presented that is based on a Bayesian maximum-likelihood formulation with strict constraints incorporated by using nonlinear reparameterizations. Quantitative simulation of imaging through atmospheric turbulence and wave-front sensing are used to demonstrate the superresolution performance of the algorithms.

Hyperspectral image compression using entropy-constrained predictive trellis coded quantization

A training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme is presented for encoding autoregressive sources. For encoding a first-order Gauss-Markov source, the mean squared error (MSE) performance of an eight-state ECPTCQ system exceeds that of entropy-constrained differential pulse code modulation (ECDPCM) by up to 1.0 dB. In addition, a hyperspectral image compression system is developed, which utilizes ECPTCQ. A hyperspectral image sequence compressed at 0.125 b/pixel/band retains an average peak signal-to-noise ratio (PSNR) of greater than 43 dB over the spectral bands.

A multiresolution approach to computer verification of handwritten signatures

We took a multi-resolution approach to the signature verification problem. The top-level representation of signatures was the global geometric features. A multi-resolution representation of signatures was obtained using the wavelet transformation. We built VQ and network classifiers to demonstrate the advantages of the multi-resolution approach. High verification rates were achieved based on a limited database.

Prediction Of Correlation Errors In Stereo-Pair Images

Machines that have been developed to determine the topography of a region from stereo-pair photographs are subject to correlation errors resulting in incorrect elevation calculations. In this paper, the determination of the location of the peak of the correlation function of two similar arrays of picture elements is modeled as a parameter estimation problem. The Cramer-Rao lower bound and various measures of contrast are considered as possible features in recognition schemes for predicting the magnitude of correlation errors. Preliminary simulation results are included.