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Frame selection using quality sharpness metrics have been shown in previous AFIT theses, to be effective in improving the final product of images obtained using adaptive optics. This thesis extends this idea to noncompensated speckle image data. Speckle image reconstruction is simulated with and without frame selection. Speckle images require the processing of hundreds of data frames. Frame selection is a method of reducing the amount of data required to reconstruct the image. A collection of short exposure image data frames of a single object are sorted based on sharpness metrics. Only the highest quality frames are retained and processed for the final image. The phase spectrum is reconstructed using the bispectrum technique. The benefits of frame selection for point (star) sources and extended (satellite) sources are examined by comparing composite image data with and without frame selection. The resulting power spectrum is evaluated through the SNR gain measurements, and the resulting phase spectrum is evaluated by measuring the phase error between the composite image and the object. In both cases, the results show that frame selection does not improve the power or the phase spectrums. For point sources, results show frame selection causes slight decrease in performance. For extended sources, the change in performance is insignificant. However, frame selection does offer a means for data reduction without significantly reducing performance in a wide variety of target brightness levels and atmospheric turbulence conditions. (AN).
Learn how to overcome resolution limitations caused by atmospheric turbulence in Imaging Through Turbulence. This hands-on book thoroughly discusses the nature of turbulence effects on optical imaging systems, techniques used to overcome these effects, performance analysis methods, and representative examples of performance. Neatly pulling together widely scattered material, it covers Fourier and statistical optics, turbulence effects on imaging systems, simulation of turbulence effects and correction techniques, speckle imaging, adaptive optics, and hybrid imaging. Imaging Through Turbulence is written in tutorial style, logically guiding you through these essential topics. It helps you bring down to earth the complexities of coping with turbulence.
Speckle imaging techniques make it possible to do high-resolution imaging through the turbulent atmosphere by collecting and processing a large number of short-exposure frames, each of which effectively freezes the atmosphere. In severe seeing condition, when the characteristic scale of atmospheric fluctuations is much smaller than the diameter of the telescope, the reconstructed image is dominated by?turbulence noise? caused by redundant baselines in the pupil. I describe a generalization of aperture masking interferometery that dramatically improves imaging performance in this regime. The approach is to partition the aperture into annuli, form the bispectra of the focal plane images formed from each annulus, and recombine them into a synthesized bispectrum form which the object may be retrieved. This may be implemented using multiple cameras and special mirrors, or with a single camera and a suitable pupil phase mask. I report results from simulations as well as experimental results using telescopes at the Air Force Research Lab's Maui Space Surveillance Site.
We will compare speckle imaging reconstruction results for several speckle imaging approaches. In particular, we will compare and contrast four methods: 1) Knox-Thompson, using a hidden phase-finder in the object spectrum phase reconstruction; 2) Knox-Thompson, using a phasor-based phase reconstruction; 3) Bispectrum, using only two bispectrum planes; Bispectrum, using four bispectrum planes. In each application of the four approaches we first calculate the modulus of the object spectrum using a Wiener-Helstrom filter to remove the speckle transfer function. The methods then differ only in their object spectrum phase reconstructions. In the simulations, we will assume that the only aberrations are those introduced by atmospheric turbulence, setting the ratio of the telescope diameter, D, to the Fried Parameter equal to ten. Additionally, we assume that the focal-plane detector array is photon-noise limited, the illumination is narrow-band (essentially monochromatic) and the atmosphere is static during each data frame. First, we will implement all four methods on a simple binary star object at low photon-per-frame light levels. Next, we will apply the methods to complex extended objects.
The removal of atmospheric turbulence (AT) distortion in long range imaging is one of the most challenging areas of research in imaging processing with an immediate need for solutions in several applications such as in military and transportation systems. AT exacerbates distortion due to non-linear geometric blur and scintillations in long-distance images and videos, severely reducing image quality and information interpretation. AT negatively impacts both human and computer vision systems, compromising visibility essential for accurate object identification and tracking. In this dissertation, a novel sparse analysis framework is developed to address efficient AT blur and scintillation removal in video. Operating under the premise that distortion-free images should be sparse in a transform domain, the application of the dual-tree complex wavelet transform is utilized on frame bursts, allowing for a new near shift-invariant complex transform space that results in higher sparsity, higher object tracking accuracy, and better resilience against camera shake, geometric distortion, and imperfect frame registration encountered in real-world AT-distorted sequences. Using this new complex transform space the novel Frame-Burst Coefficient Shimmer Thresholding (FBST) algorithm is developed. FBST considers the complex coefficient shimmer across multiple frames to address threshold selection and moving object blur, issues still present in other methods which utilize techniques such as averaging and empirical threshold selection. In fact, by evaluating video sequences of moving vehicles with visible license plates, we show FBST produces up to an 85% sparse reconstruction with superior visual results compared to weighted and simple thresholding approaches while preserving object motion, reducing AT distortion, and enhancing object contrast and visibility. Moreover, compressed sensing (CS) methods to sparse AT distortion removal are also investigated through direct CS sampling of the coefficients of the complex wavelet transform, allowing us to sparsely sample and reconstruct a restored output image. This essentially develops a means to sample in real-time, store the reduced size dataset, and reconstruct a de-blurred output in the cloud for low-power portable systems. Results using AT-distorted traffic sequences indicate that CS provides up to a 75% sparse reconstruction, contrast enhancement, and de-blurring, while using only one-fifth the number of coefficients for sampling. Overall, this dissertation presents a sparse analysis framework that is well suited to provide robust AT distortion removal that does not blur moving objects and provides a highly sparse representation with reduced storage requirements for distributed-processing and low-power monitoring systems.
An extensive empirical study of the frame selection method of high-resolution astronomical imaging, also known as lucky imaging, has been carried out. This method involves the capture of many hundreds or thousands of short exposure frames, the scanning and ranking of all the frames for sharpness, and the co-alignment and averaging of only the best frames into a final image. The performance of the selective imaging technique was tested by imaging several bright stars, binaries and clusters, and using the brightest pixel value in each frame as the sharpness metric. The lucky imaging parameters are the frame exposure time, telescope aperture, seeing, frame selection fraction, colour band, and distance from the co-alignment position. By using a range of aperture masks and colour filters, and a camera with adjustable exposure, we explored the widest range of parameter space of any frame selection study to date. -- The results from the stellar images show that frame selection is very effective at reducing the effects of atmospheric turbulence on astronomical images. When all frames in an observation are combined, the seeing disc is diminished and a bright central core is apparent. By selecting just the best 1% of frames the core is brighter while the halo almost vanishes. In many of these cases faint spikes and dark rings appeared, indicating difraction limited images. The results were best when small apertures were used, particularly at longer wavelengths. It was found that, for the Siding Spring site, the exposure time should be limited to 10 ms, although images were improved at all times tested. It was also found that when image frames of binaries and clusters were co-aligned on the brightest star, the improvement in sharpness was greatest at this point, and the improvement decreased with angular radius. -- To prepare for future research, solar system planets and lunar features were also imaged. These were processed with sharpness metrics suited to extended objects. It was found that frame selection was effective at improving the planetary images over traditional long exposure images. However the results were not as good as for the stellar images. Possible reasons for this are discussed.
Learn how to overcome resolution limitations caused by atmospheric turbulence in Imaging Through Turbulence. This hands-on book thoroughly discusses the nature of turbulence effects on optical imaging systems, techniques used to overcome these effects, performance analysis methods, and representative examples of performance. Neatly pulling together widely scattered material, it covers Fourier and statistical optics, turbulence effects on imaging systems, simulation of turbulence effects and correction techniques, speckle imaging, adaptive optics, and hybrid imaging. Imaging Through Turbulence is written in tutorial style, logically guiding you through these essential topics. It helps you bring down to earth the complexities of coping with turbulence.
Even today, when many large telescopes have high performance Adaptive Optics systems, speckle imaging is as important as ever to get useful high-spatial-resolution information about important astronomical objects, particularly in the detection of stellar companions of exoplanet host stars. Any methods that can extend the reach of speckle imaging to objects that are fainter or farther away would be welcome additions to the range of techniques for high-resolution imaging. ¶ In the first part of the thesis I describe a method to do a selection of frames of an image stack from an astronomical observation done by speckle imaging, using specific Rényi entropy as a classification, that is, a decision-making tool. With this automatic selection process the signal-to-noise ratio can be improved between 15-20% in some cases, although no method could consistently provide improvement in all cases. Another implication of the study is that, by using entropy as a criterion, a sub-stack of 10 to 40% of the "best" frames in a speckle stack can often demonstrate a signal-to-noise ratio comparable to a full stack, which could minimize computation time of the reconstructed image, making it feasible to compute at the telescope. ¶ In the second part of the thesis I describe how the data from a Shack-Hartmann sensor, as an a priori information source, can be used to create a better instantaneous Point-Spread-Function (PSF) that can be used to preprocess speckle data, leading to a scientific image with higher S/N and better detection limits for faint companions. This is of some importance in certain science applications of speckle imaging observations, such as the accurate measurement of the radii of exoplanets.