Glen Archer

Comparison of bispectrum, multiframe blind deconvolution and hybrid bispectrum-multiframe blind deconvolution image reconstruction techniques for anisoplanatic, long horizontal-path imaging

Abstract. The potential benefits of real-time, or near-real-time, image processing hardware to correct for turbulence-induced image defects for long-range surveillance and weapons targeting are sufficient to motivate significant resource commitment to their development. Quantitative comparisons between potential candidates are necessary to decide on a preferred processing algorithm. We begin by comparing the mean-square-error (MSE) performance of speckle imaging (SI) methods and multiframe blind deconvolution (MFBD), applied to long-path horizontal imaging of a static scene under anisoplanatic seeing conditions. Both methods are used to reconstruct a scene from three sets of 1000 simulated images featuring low, moderate, and severe turbulence-induced aberrations. The comparison shows that SI techniques can reduce the MSE up to 47%, using 15 input frames under daytime conditions. The MFBD method provides up to 40% improvement in MSE under the same conditions. The performance comparison is repeated under three diminishing light conditions, 30, 15, 8 photons per pixel on average, where improvements of up to 39% can be achieved using SI methods with 25 input frames, and up to 38% for the MFBD method using 150 input frames. The MFBD estimator is applied to three sets of field data and representative results presented. Finally, the performance of a hybrid bispectrum-MFBD estimator that uses a rapid bispectrum estimate as the starting point for the MFBD image reconstruction algorithm is examined.

Using speckle imaging techniques as a starting point for MFBD scene reconstruction from long horizontal-path, turbulence-degraded imagery

Recent works indicate that both MFBD and speckle imaging methods are e ective in recovering images of scenes from sets of turbulence-degraded imagery acquired over long horizontal paths. In this work, a prototype scene estimate, generated using speckle-imaging methods, is used in place of the multi-frame ensemble average, to initialize the iterative MFBD algorithm. Available performance improvements are described quantitatively by examining the improvement in Mean Squared Error (MSE) compared to a di raction-limited image. When speckle image estimates initialize the MFBD algorithm residual MSE is reduced by 16% on average compared the case where the multi-frame average is used as a starting point. Similarly, residual MSE is reduced another 8% beyond what is available using speckle imaging method alone when the number of iterations is not constrained. We also nd that the variation in reconstruction MSE is reduced signi cantly using only a limited number of iterations when subject to low to moderated image degradation compared to speckle imaging alone.

Reconstruction of long horizontal-path images under anisoplanatic conditions using multiframe blind deconvolution

All optical systems that operate in or through the atmosphere suffer from turbulence induced image blur. Both military and civilian surveillance, gun sighting, and target identification systems are interested in terrestrial imaging over very long horizontal paths, but atmospheric turbulence can blur the resulting images beyond usefulness. This work explores the mean square error (MSE) performance of a multiframe blind deconvolution (MFBD) technique applied under anisoplanatic conditions for both Gaussian and Poisson noise model assumptions. The technique is evaluated for use in reconstructing images of scenes corrupted by turbulence in long horizontal-path imaging scenarios. Performance is evaluated via the reconstruction of a common object from three sets of simulated turbulence degraded imagery representing low, moderate, and severe turbulence conditions. Each set consisted of 1000 simulated turbulence degraded images. The MSE performance of the estimator is evaluated as a function of the number of images, and the number of Zernike polynomial terms used to characterize the point spread function. A Gaussian noise model-based MFBD algorithm reconstructs objects that showed as much as 40% improvement in MSE with as few as 14 frames and 30 Zernike coefficients used in the reconstruction, despite the presence of anisoplanatism in the data. An MFBD algorithm based on the Poisson noise model required a minimum of 50 frames to achieve significant improvement over the average MSE for the data set. Reconstructed objects show as much as 38% improvement in MSE using 175 frames and 30 Zernike coefficients in the reconstruction.

Mean squared error performance of MFBD nonlinear scene reconstruction using speckle imaging in horizontal imaging applications

Terrestrial imaging over very long horizontal paths is increasingly common in surveillance and defense systems. All optical systems that operate in or through the atmosphere suffer from turbulence induced image blur. This paper explores the Mean-Square-Error (MSE) performance of a multi-frame-blind-deconvolution-based reconstruction technique using a non-linear optimization strategy to recover a reconstructed object. Three sets of 70 images representing low, moderate and severe turbulence degraded images were simulated from a diffraction limited image taken with a professional digital camera. Reconstructed objects showed significant, 54, 22 and 14 percent improvement in mean squared error for low, moderate, and severe turbulence cases respectively.

Working on how to solve the never ending problem of diversity

There are many challenges in changing the diversity in engineering. In the Catalyzing Collaborative Conversation, we explored ways to change the diversity climate through outreach, recruitment, and retention methods to help students succeed and achieve the goal of becoming an engineer.

Perspective of teenagers on traits and research associated with electrical and Computer Engineers and their research

Gender and diversity balance issues are prominent in the field of engineering. The way engineers and their research are perceived are two areas that contribute to youth deciding on careers in science and engineering. The perceptions of the youth on traits of Electrical and Computer Engineers (ECEs) and their research were explored through a survey of summer youth program students at Michigan Tech. Five different week long engineering programs were offered with surveys presented at the start and end of the contact times to observe how the activities and outreach impact the student perceptions. The perspectives of the youth show a lack of gender related trait association. After all different levels of contact, research area association with ECE was expanded. The largest areas displaying increased association were socially impacted areas, such as medicine. Increasing the association with these areas may help increase the interest of women in becoming engineers using the demographics of females in sciences as a statistical guide. This has shown that the benefit of engineering programs on perceptions and association is indifferent to curriculum and contact duration; that every bit of outreach makes a difference for the youth and prospects for improving gender balance in engineering.

Parallel hybrid bispectrum-multi-frame blind deconvolution image reconstruction technique

AbstractThis paper presents B-MFBD, a parallel hybrid of bispectrum speckle imaging (SI) and multi-frame blind deconvolution (MFBD) image reconstruction techniques for anisoplanatic, long horizontal path imaging. Our aim is to recover an enhanced version of a turbulence-corrupted image by massive parallelization of an SI and MFBD algorithms. The bispectrum SI technique is used in place of the multi-frame ensemble averaging to initialize the iterative parallel MFBD algorithm. B-MFBD technique, through massive parallelization, provides significantly large improvement in execution speed to both the bispectrum SI and MFBD parts of the hybrid algorithm. We report

Understanding similarities and differences in students across first-year computing majors

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