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Performance of three reconstruction methods on blurred and noisy images of extended scenes

机译:三种重建方法在扩展场景的模糊和嘈杂图像上的性能

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Abstract: Reconstructed scene intensity distribution obtained from deconvolution using three different iterative reconstruction methods: phase diversity, deconvolution, and iterative blind deconvolution approaches are presented. For images degraded with as much as a quarter wavelength of aberration and a signal to noise ratio of 10, we show that the correlation between the `truth' scene and the reconstructed scene is 0.9761 for deconvolution, 0.9680 for phase diversity, and 0.9169 for iterative blind deconvolution. The correlation coefficient becomes even higher as the signal to noise ratio and the aberration strength decrease. In spite of the sometimes severe edge effect, we show that these algorithms as adapted by our group yield relatively decent reconstructed objects as determined visually and by peak correlation coefficient comparison. The success of these adapted algorithms on extended scenes makes them potentially useful in imaging with degraded optical systems.!13
机译:摘要:提出了使用三种不同的迭代重建方法从反卷积获得的重构场景强度分布:相位分集,反卷积和迭代盲反卷积方法。对于以高达四分之一的像差波长和10的信噪比退化的图像,我们表明“真实”场景与重建场景之间的相关性对于反卷积为0.9761,对于相位分集为0.9680,对于迭代为0.9169盲反卷积。随着信噪比和像差强度降低,相关系数变得更高。尽管有时会出现严重的边缘效应,但我们证明,通过我们的小组进行调整的这些算法,可以通过视觉和峰值相关系数比较来确定相对不错的重建对象。这些适应性算法在扩展场景上的成功使用,使其在光学系统退化的成像中可能很有用。13

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