Simultaneously estimating position x and velocity v of moving targets using only the measured phase ' from single-channel SAR is impossible because the mapping from (x, v) to φis many-to-one. This paper defines classes of equivalent target motion and solves the GMTI problem up to membership in an equivalence class using single-channel SAR phase data. Definitions are presented for endo- and exo-clutter that are consistent with the equivalence classes, and it is shown that most target motion can be detected, i.e. the set of endo-clutter targets is very small. We exploit the sparsity of moving targets in the scene to develop an algorithm to resolve target motion up to membership in an equivalence class, and demonstrate the effectiveness of the proposed technique using simulated data.
KEYWORDS: Scanning electron microscopy, Image restoration, Compressed sensing, Sensors, Electron microscopes, Electron microscopy, Image compression, Raster graphics, Atomic force microscopy, Signal to noise ratio
Scanning electron microscopes (SEMs) are used in neuroscience and materials science to image centimeters of sample area at nanometer scales. Since imaging rates are in large part SNR-limited, large collections can lead to weeks of around-the-clock imaging time. To increase data collection speed, we propose and demonstrate on an operational SEM a fast method to sparsely sample and reconstruct smooth images. To accurately localize the electron probe
position at fast scan rates, we model the dynamics of the scan coils, and use the model to rapidly and accurately visit a randomly selected subset of pixel locations. Images are reconstructed from the undersampled data by compressed sensing inversion using image smoothness as a prior. We report image fidelity as a function of acquisition speed by comparing traditional raster to sparse imaging modes. Our approach is equally applicable to other domains of
nanometer microscopy in which the time to position a probe is a limiting factor (e.g., atomic force microscopy), or in which excessive electron doses might otherwise alter the sample being observed (e.g., scanning transmission electron microscopy).
We report on subjective experiments comparing example-based regularization, total variation regularization,
and the joint use of both regularizers. We focus on the noisy deblurring problem, which generalizes image
superresolution and denoising. Controlled subjective experiments suggest that joint example-based regularization
and total variation regularization can provide subjective gains over total regularization alone, particularly when
the example images contain similar structural elements as the test image. We also investigate whether the
regularization parameters can be trained by cross-validation, and we compare the reconstructions using crossvalidation
judgments made by humans or by fully automatic image quality metrics. Experiments showed that of
five image quality metrics tested, the structural similarity index (SSIM) correlates best with human judgement
of image quality, and can be profitably used to cross-validate regularization parameters. However, there is a
significant quality gap between images restored using human or automatic parameter cross-validation.
We investigate a Wiener fusion method to optimally combine multiple estimates
for the problem of image deblurring given a known blur and a corpus of sharper training images.
Nearest-neighbor estimation of high frequency information from training images is fused
with a standard Wiener deconvolution estimate. Results show an improvement in sharpness
and decreased artifacts compared to either the standard Wiener filter or the nearest-neighbor
reconstruction.
We assess the impact of supplementing two-dimensional video with three-dimensional geometry for persistent vehicle
tracking in complex urban environments. Using recent video data collected over a city with minimal terrain content, we
first quantify erroneous sources of automated tracking termination and identify those which could be ameliorated by
detailed height maps. They include imagery misregistration, roadway occlusion and vehicle deceleration. We next
develop mathematical models to analyze the tracking value of spatial geometry knowledge in general and high resolution
ladar imagery in particular. Simulation results demonstrate how 3D information could eliminate large numbers of false
tracks passing through impenetrable structures. Spurious track rejection would permit Kalman filter coasting times to be
significantly increased. Track lifetimes for vehicles occluded by trees and buildings as well as for cars slowing down at
corners and intersections could consequently be prolonged. We find high resolution 3D imagery can ideally yield an
83% reduction in the rate of automated tracking failure.
A prototype image processing system has recently been developed which generates, displays and analyzes threedimensional ladar data in real time. It is based upon a suite of novel algorithms that transform raw ladar data into cleaned 3D images. These algorithms perform noise reduction, ground plane identification, detector response deconvolution and illumination pattern renormalization. The system also discriminates static from dynamic objects in a scene. In order to achieve real-time throughput, we have parallelized these algorithms on a Linux cluster. We demonstrate that multiprocessor software plus Blade hardware result in a compact, real-time imagery generation adjunct to an operating ladar. Finally, we discuss several directions for future work, including automatic recognition of moving people, real-time reconnaissance from mobile platforms, and fusion of ladar plus video imagery. Such enhancements of our prototype imaging system can lead to multiple military and civilian applications of national importance.
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