The problem of target detection in a sequence of infrared images is not an easy task when the target is small, faint and obscured. The problem grows more complex when the target is embedded in a highly structured (correlated) background. In this paper, a new detector for small IR targets is proposed. This detector consists of three main components: a local whitening (demeaning) filter, an orthogonal image modeling algorithm, known as fast orthogonal search (FOS), and finally a first order statistical analysis is exploited for further reduction of false alarms. Experimental results of using the above detector to detect real infrared targets are also included. These results demonstrate that the new detector yields a promising solution for the detection problems of small targets.
We have developed a method to estimate both the original objects and the blurring function from a sequence of noisy blurred images, simultaneously collected at different wavelengths (wavelength diversity). The assumption of common path-length errors across the diversity channels allows for a parallel deconvolution procedure that exploits this coupling. In contrast with previous work, no a priori assumptions about the object's intensity distribution are required. The method is described, and preliminary results for both synthetic computer-generated images and real images collected with a bench-scale imaging system are presented, demonstrating the promise of the algorithm.
The resolution of images captured through ground-based telescopes is generally limited by blurring effects due to atmospheric turbulence. We have developed a method to estimate both the original objects and the blurring function from a sequence of noisy blurred images, simultaneously collected at different wavelengths (wavelength diversity). The assumption of common path-length errors across the diversity channels allows for a parallel deconvolution procedure that exploits this coupling. In contrast with previous work, no a priori assumptions about the object’s intensity distribution are required. The method is described, and preliminary results with real images collected with a bench-scale imaging system are presented, demonstrating the promise of the algorithm.
It is well known that the overall performance of the automatic imaging target recognition system is strongly affected by the used detection technique. Recently, the wavelet and matching pursuit methods are merged together as an excellent methodology for detecting targets in a sequence of infrared images with high detection rate, and low false alarms. The wavelet transform is used as a detector of the regions of interest, which may include false alarms, while the matching pursuit uses the known target's features to reduce the clutters (or false alarms) from the wavelet output. Only the non-orthogonal matching pursuit is used for this purpose because its orthogonal version is more computationally expensive. This prevents the exploitation of the orthogonal matching pursuit, which can provide image modeling with less number of terms that can significantly faciliate the target extraction and clutter reduction. In this paper, we introduce the usage of the fast orthogonal search method, which is an orthogonal modeling technique, instead of matching pursuit for small infrared imaging target detection. The fast orthogonal search performs the orthogonalization process in more efficient way, so its computational time is much less than the original orthogonal matching pursuit. Moreover, the fast orthogonal search provides a precise extraction of the target's model parameters that may be used for tracking purposes.
The low-frequency behavior of atmospherically distorted wavefronts is not accurately known at this time. It is expected that the phase delays will be a zero mean random process. A new zero-low-frequency model of spatial PSD of the phase distortions is presented in this paper. This model has a low frequency, called the roll-over frequency, for which the PSD starts to approach zero as the frequency decreases and approaches zero. This roll-over frequency is proportional to the outer-scale of turbulence. To verify the zero-low- frequency model, wavefront slope data was analyzed from the GSM experiment with a nonlinear modelling technique called Robust Orthogonal Search (ROS). One property of ROS is that it can detect frequency components at frequencies lower than the resolution of the discrete Fourier transform. This investigation consistently indicates a lowest frequency in the data for which any lower frequency components have lower power. This results supports the low-frequency model which rolls over and approaches zero for low spatial frequencies.
The simulation and performance evaluation of high-order, adaptive-optics (AO) corrective systems for the new, large telescopes require the generation of distorted wavefronts (WF) that accurately model the behavior of the real atmosphere. Roggemann et al. have introduced a method of accurately generating distorted wavefronts based on the covariance matrix of wavefront distortions and Cholesky factorization. Several authors have proposed fractional-Brownian-motion algorithms that are more computationally efficient as alternatives for generating distorted wavefronts but do not give accurate correlations. In this paper, the fractional Gaussian process (FGP) algorithm is introduced as a new method of generating distorted wavefronts. The algorithm is presented on both one and two dimensions. The correlation function and power spectra of simulated wavefronts are shown to compare well to the expected correlation and power spectra respectively. It is concluded that the FGP algorithm is an accurate and efficient method of generating simulated wavefronts.
Kolmogorov turbulence has a temporal behavior best described by fractional Brownian motion (FBM) with a self-similarity parameter of H equals 5/6. Non-Kolmogorov behavior, which has also been observed and reported, is characterized by H other than 5/6. Although FBM has strong inter-sample correlations, the signals arising in an adaptive optics control loop are not FBM, have weaker inter-sample correlation,and exhibit quite different characteristics with regard to predictability. One conclusion is that for greatest advantage, prediction within the loop should be introduced at the point where signals represent the reconstructed wavefront, i.e. mirror drive signals, and not at the level of the wavefront sensor output or wavefront error signals.
Atmospherically-induced wavefront distortions that follow a Kolmogorov power spectral density has a spatial and
temporal behaviour best described as a fractional Brownian motion (FBM) process. FBM is persistent and has a
considerable degree of predictability, but its slopes are antipersistent and are not easily predicted by conventional means.
However, previous measurements have shown that there is a exploitable degree of persistence and predictability in
wavefront slopes. In this paper we describe how lowpass-filtered FBM can model the spectral, temporal and predictive
properties of distorted wavefronts. Implications for closed-loop adaptive optics systems will be discussed.