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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Optimal defocus estimation in individual natural images
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Optimal defocus estimation in individual natural images

机译：单个自然图像中的最佳散焦估计

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摘要

Defocus blur is nearly always present in natural images: Objects at only one distance can be perfectly focused. Images of objects at other distances are blurred by an amount depending on pupil di ameter and lens properties. Despite the fact that defocus is of great behavioral, perceptual, and biological importance, it is unknown how biological systems estimate defocus. Given a set of natural scenes and the properties of the vision system, we show from first principles how to optimally estimate defocus at each location in any individual image. We show for the human visual system that high precision, unbiased estimates are obtainable under natural viewing conditions for patches with detectable contrast. The high quality of the estimates is surprising given the heterogeneity of natural images. Additionally, we quantify the degree to which the sign ambiguity often attributed to defocus is resolved by monochro matic aberrations (other than defocus) and chromatic aberrations; chromatic aberrations fully resolve the sign ambiguity. Finally, we show that simple spatial and spatio-chromatic receptive fields ex tract the information optimally. The approach can be tailored to any environment-vision system pairing: natural or man-made, animal or machine. Thus, it provides a principled general framework for ana lyzing the psychophysics and neurophysiology of defocus estima tion in species across the animal kingdom and for developing optimal image-based defocus and depth estimation algorithms for computational vision systems.
机译：自然图像中几乎总是会出现散焦模糊：只有一个距离的物体才能被完美聚焦。根据瞳孔直径和镜头特性，其他距离的物体的图像会模糊不清。尽管散焦具有重要的行为，感知和生物学重要性，但尚不清楚生物系统如何估计散焦。给定一组自然场景和视觉系统的属性，我们从第一原理开始说明如何最佳估计任何单个图像中每个位置的散焦。我们为人类的视觉系统显示，在自然观察条件下，具有可检测对比度的色块可获得高精度，无偏估计。考虑到自然图像的异质性，估计的高质量令人惊讶。此外，我们量化了通常由散焦引起的符号模糊度通过单色像差（散焦除外）和色差解决的程度；色差完全解决了符号模糊的问题。最后，我们表明简单的空间和时空色接收域可以最佳地提取信息。该方法可以针对任何环境视觉系统配对量身定制：自然或人造，动物或机器。因此，它为分析整个动物界中物种的散焦估计的心理物理学和神经生理学，以及为计算视觉系统开发基于图像的最佳散焦和深度估计算法，提供了一个有原则的通用框架。

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