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Application of mfbd algorithms to image reconstruction under anisoplanatic conditions.

机译:mfbd算法在各向异性条件下的图像重建中的应用。

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摘要

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. My dissertation explores the performance of a multi-frame-blind-deconvolution 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 and compared to other speckle imaging techniques. 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.;I will compare the mean-square-error (MSE) performance of speckle imaging methods and a maximum-likelihood, multi-frame blind deconvolution (MFBD) method applied to long-path 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. The MFBD estimator is applied to three sets of field data and the results presented. Finally, a combined Bispectrum-MFBD Hybrid estimator is proposed and investigated. This technique consistently provides a lower MSE and smaller variance in the estimate under all three simulated turbulence conditions.
机译:在大气中或通过大气运行的所有光学系统都会遭受湍流引起的图像模糊。军事和民用监视,瞄准镜和目标识别系统都对很长的水平路径上的地面成像感兴趣,但是大气湍流会使产生的图像模糊不清。本文探讨了在各向异性平面条件下对高斯和泊松噪声模型假设应用的多帧盲去卷积技术的性能。对该技术进行了评估,以用于重建在长水平路径成像场景中被湍流破坏的场景图像,并与其他散斑成像技术进行了比较。通过从代表低,中和严重湍流条件的三组模拟湍流退化图像中重建公共对象来评估性能。每组包括1000张模拟的湍流退化图像。估计器的MSE性能根据图像数量以及用于表征点扩散函数的Zernike多项式项的数量进行评估。我将比较散斑成像方法的均方误差(MSE)性能最大似然多帧盲反卷积(MFBD)方法应用于长路径水平成像场景。两种方法都用于从具有等平面湍流引起的像差的模拟图像重建场景。对三组每组1000张模拟图像进行比较,每组图像分别针对低,中和严重湍流引起的图像退化。比较表明,在白天,中等帧频下使用15个输入帧,散斑成像技术分别在低,中和重度情况下分别将MSE平均降低46%,42%和47%。同样,在相同条件下,MFBD方法平均可将MSE分别提高40%,29%和36%。在弱光条件下(每个像素少于100个光子)重复比较,使用斑点成像方法可得到39%,29%和27%的改进,而MFBD则可提供25个输入帧和38%,34%和33%的改进方法和150个输入帧。 MFBD估计器应用于三组现场数据,并给出了结果。最后,提出并研究了组合的双频谱-MFBD混合估计器。在所有三种模拟湍流条件下,该技术始终提供较低的MSE和较小的估计方差。

著录项

  • 作者

    Archer, Glen E.;

  • 作者单位

    Michigan Technological University.;

  • 授予单位 Michigan Technological University.;
  • 学科 Electrical engineering.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 155 p.
  • 总页数 155
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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