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.
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.
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.
The potential benefits of real-time, or near-real-time, turbulent image processing hardware for long-range surveillance and weapons targeting are sufficient to motivate significant commitment of both time and money to their development. Thoughtful comparisons between potential candidates are necessary to confidently decide on a preferred processing algorithm. In this paper, we compare the mean-square-error (MSE) performance of speckle imaging methods and a maximum-likelihood, multi-frame blind deconvolution (MFBD) method applied to longpath horizontal imaging scenarios. Both methods are used to reconstruct a scene from simulated imagery featuring anisoplanatic turbulence induced aberrations. This comparison is performed over three sets of 1000 simulated images each for low, moderate and severe turbulence-induced image degradation. The comparison shows that speckle-imaging techniques reduce the MSE 46 percent, 42 percent and 47 percent on average for low, moderate, and severe cases, respectively using 15 input frames under daytime conditions and moderate frame rates. Similarly, the MFBD method provides, 40 percent, 29 percent, and 36 percent improvements in MSE on average under the same conditions. The comparison is repeated under low light conditions (less than 100 photons per pixel) where improvements of 39 percent, 29 percent and 27 percent are available using speckle imaging methods and 25 input frames and 38 percent, 34 percent and 33 percent respectively for the MFBD method and 150 input frames.
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.
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