Peter D. Gianino

Homodyne and heterodyne imaging through a scattering medium.

We introduce a novel two-dimensional (2D) homodyne and heterodyne technique for imaging objects through or embedded in a scattering medium. Our imaging approach is based on heterodyning of light with different Doppler broadenings that is scattered from objects of two different textures or from an opaque object and a textured scattering medium. We report on the initial demonstration of pulling signals out of noise for an object hidden behind a scattering medium. Enhancements of signal-to-noise ratio of the order of 50 have been achieved by use of a 2D holographic phase-sensitive detector. We also discuss the experimental feasibility of this approach for objects embedded in a scattering medium.

Encoded filter for heterogeneous correlation

In this paper we introduce a new algorithm for an encoded filter for heterogeneous correlation by enhancing the cross- correlation between selected different objects. The new algorithm should allow the expansion of the use of correlation systems from recognition to classification. We tested the feasibility of this approach using a data base of stored information.

Synthetic aperture radar image correlation by use of preprocessing for enhancement of scattering centers

We demonstrate that a significant improvement can be obtained in the recognition of complicated synthetic aperture radar images taken from the Moving and Stationary Target Acquisitions and Recognition database. These images typically have a low number of scattering centers and high noise. We first preprocess the images and the templates formed from them so that their scattering centers are enhanced. Our technique can produce high-quality performance in several correlation criteria. For realistic automatic target recognition systems, our approach should make it easy to implement optical recognition systems with binarized data for many different types of correlation filter and should have a great effect on feeding data-compressed (binarized) information into either digital or optical processors.

Speckle velocimetry by means of holographic time-integrative correlation

We develop the theory of the speckle velocimeter that is based on use of a photorefractive real-time hologram in four-wave mixing as a time-integrative correlator. The theory of the speckle velocimeter has been developed for the time correlation between the far-field spectrum of light scattered from the diffuser and the reference wave that is Doppler shifted. Our theoretical derivation shows that it is possible to extract the velocity with minor processing of the output correlation.

Optimal synthetic aperture radar image correlation using enhanced scattering centers in holographic data storage

We prove that for gray-level or binarized synthetic aperture radar (SAR) images with enhanced scattering centers, the DC-blocked phase-only filter is the optimal, as well as the most practical, solution for SAR image recognition. Our correlation algorithm, which employs various power laws to enhance the scattering centers, was examined for images with different complexity using the moving and stationary target acquisitions and recognition (MSTAR) data base. For standard recognition problems, which represent 95% of the cases (intermediate level of noise and sufficient number of scattering centers on the target), we found that our proposed approach improves the correlation even when utilizing binary templates extracted from the region of interest and binary inputs. For more complex problems (representing nearly 5% of the cases), a further improvement in our correlation recognition approach is needed.

Phase-restricted heterogeneous correlation

We introduce a new phase-restricted algorithm for producing a heterogeneous correlation filter that permits new in-class members to be added without changing the phase of the filter. This heterogeneous correlation filter uses amplitude modulation both to enhance in-class cross correlations and to suppress selected out-of-class correlations. This new algorithm should substantially improve the performance of existing class-associative correlators and improve their operation.

Heteroassociative correlation filter using cross-correlation enhancement

We introduce a new fixed-phase algorithm for producing an object recognition correlation filter by either enhancing or suppressing unnecessary spectrum information from selected objects. We demonstrate this approach by simulating the performance using five test images with completely dissimilar edge information. This new algorithm can be used to substantially improve the performance of existing composite classification and recognition filter algorithms.

Nonlinear transformation analysis of the misregistered trade-off heterocorrelation filter: part 2, using the phase-only filter

We continue our study of the misregistered trade-off heterocorrelation filter (HCF), introduced in Part 1. Instead of using the matched filter, we use only the phases of the Fourier transforms of the functions constituting the filter, with our basic filter being the phase-only filter. We show that by adjusting the value of the trade-off factor it is possible to make the correlation pattern and intensities of the heterocorrelation peaks equal to those of the autocorrelation peaks; in effect, making the HCF a homogeneous filter. This means that it can equally recognize totally different objects that one designs it to recognize. These results (1) turn out to be independent of the amount of misregistration (i.e., shift) between the centers of the impulse responses of the functions making up the filter and (2) can also support the claim that a HCF is a totally new approach to generating synthetic discriminant filter functions. We also produce plots showing how the intensities, peak-to-noise ratios, and peak-to-secondary ratios of both autocorrelation and heterocorrelation peaks behave as a function of trade-off factor.

Companding nonlinear correlators

In this paper we summarize a new category of all optical companding nonlinear correlators developed by our group in the last decade. All optical companding nonlinear correlators consist oftwo families: The first is based on energy transfer between the joint spectra of reference and signal images. The second family is based on incoherent erasure of a grating formed by coupled beams . Allof these correlators have similar features. Therefore, we take one representative case of study, namely, the photorefractive two-beam coupling correlator. We perform theoretical analysis, computer simulations and experimental demonstrations to predict the location of the best operating point of the two-beam coupling joint transform correlator. From this study we determine the best operational condition for high speed and resolution, as well as for optimal trade-off between correlation peak intensity, efficiency and noise performance. We also study the performance of compansive correlators in analogy with the limiting square law receiver. It was found that the optimal performing point corresponds to noise variance that is proportional to the transition from compression to expansion.

Multipurpose mean-square-error filter for processing obscured images

We develop a generalized minimum mean-square-error image processing filter for recognition and retrieval of noisy, blurred and obscured images. We examined the performance of this filter in four modes: (1) the well-known mean-square- error correlation filter; (2) the phase-only mean-square- error correlation filter; (3) the matched mean-square-error correlation filter, and (4) the image retrieving filter. Our simulation result show that it is possible to retrieve and recognize blurred images that are 90 percent obscured and whose signal-to-noise ratio is 0.1.