Mean squared error performance of speckle-imaging using the bispectrum in horizontal imaging applications

Bos, Jeremy P.; Roggemann, Michael C.

doi:10.1117/12.884093

Cited by 12 publications

(19 citation statements)

References 10 publications

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“…Second, we are able to use simulated data to quantitatively evaluate turbulence mitigation methods. 4,[13][14][15] Quantitative analysis is possible because we have an objective truth image from which the degraded images are generated. Without such a truth image, assessment of restoration algorithms is subjective, and optimization cannot be automated.…”

Section: Introductionmentioning

confidence: 99%

Simulation of anisoplanatic imaging through optical turbulence using numerical wave propagation with new validation analysis

et al. 2017

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, "Simulation of anisoplanatic imaging through optical turbulence using numerical wave propagation with new validation analysis," Opt. Eng. 56(7), 071502 (2017), doi: 10.1117/1.OE.56.7.071502. Abstract. We present a numerical wave propagation method for simulating imaging of an extended scene under anisoplanatic conditions. While isoplanatic simulation is relatively common, few tools are specifically designed for simulating the imaging of extended scenes under anisoplanatic conditions. We provide a complete description of the proposed simulation tool, including the wave propagation method used. Our approach computes an array of point spread functions (PSFs) for a two-dimensional grid on the object plane. The PSFs are then used in a spatially varying weighted sum operation, with an ideal image, to produce a simulated image with realistic optical turbulence degradation. The degradation includes spatially varying warping and blurring. To produce the PSF array, we generate a series of extended phase screens. Simulated point sources are numerically propagated from an array of positions on the object plane, through the phase screens, and ultimately to the focal plane of the simulated camera. Note that the optical path for each PSF will be different, and thus, pass through a different portion of the extended phase screens. These different paths give rise to a spatially varying PSF to produce anisoplanatic effects. We use a method for defining the individual phase screen statistics that we have not seen used in previous anisoplanatic simulations. We also present a validation analysis. In particular, we compare simulated outputs with the theoretical anisoplanatic tilt correlation and a derived differential tilt variance statistic. This is in addition to comparing the long-and short-exposure PSFs and isoplanatic angle. We believe this analysis represents the most thorough validation of an anisoplanatic simulation to date. The current work is also unique that we simulate and validate both constant and varying C 2 n ðzÞ profiles. Furthermore, we simulate sequences with both temporally independent and temporally correlated turbulence effects. Temporal correlation is introduced by generating even larger extended phase screens and translating this block of screens in front of the propagation area. Our validation analysis shows an excellent match between the simulation statistics and the theoretical predictions. Thus, we think this tool can be used effectively to study optical anisoplanatic turbulence and to aid in the development of image restoration methods. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Introductionmentioning

confidence: 99%

Simulation of anisoplanatic imaging through optical turbulence using numerical wave propagation with new validation analysis

et al. 2017

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“…(10) and (15) are parameterized by the object pixel intensities and aberration coefficients, and are applied to a nonlinear optimization MATLAB routine to find the object and aberration coefficients most likely to have produced the images that were simulated in the data set. The intensities at each pixel location in each image are vectorized.…”

Section: L-bfgs Optimizationmentioning

confidence: 99%

“…8 Hybrid hardware-software strategies offer the potential to produce onthe-fly estimates of scenes, but require substantial investment in both hardware and software to produce results. 9 Bos and Roggemann 10 have reported the use of software reconstruction techniques using the bi-spectrum method in nearly real time. The use of these strategies to surveillance imaging is largely unexplored.…”

Section: Introductionmentioning

confidence: 99%

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

2013

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Abstract. 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 modelbased 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.

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“…The resulting distortion will limit the performance of any optical system operating in such a turbulent atmosphere, frequently causing the observed scene to be blurred beyond usefulness. 3 Many strategies have been proposed to estimate the true scene from turbulence-corrupted images, not all of which are explored in this work. Each represents a compromise between how quickly the technique produces an estimate of the scene, the accuracy of the estimate, and the robustness of the technique.…”

Section: Introductionmentioning

confidence: 99%

“…Both Carrano and Bos ignore anisoplanatism in their processing but nevertheless produce reconstructions with reduced error. 3 Other blind deconvolution techniques have also been applied to reconstructing turbulence-corrupted images. Richardson 10 and Lucy 11 pioneered the application of an iterative blind deconvolution technique to a single image degraded by atmospheric turbulence.…”

Section: Introductionmentioning

confidence: 99%

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

2014

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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.

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Mean squared error performance of speckle-imaging using the bispectrum in horizontal imaging applications

Cited by 12 publications

References 10 publications

Simulation of anisoplanatic imaging through optical turbulence using numerical wave propagation with new validation analysis

Simulation of anisoplanatic imaging through optical turbulence using numerical wave propagation with new validation analysis

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

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

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