Optimal defocus estimation in individual natural images
Optimal defocus estimation in individual natural images
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DOI：
10.1073/pnas.1108491108
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发表时间：
2011-10-04
影响因子：
11.1
通讯作者：
Geisler, Wilson S.
中科院分区：
文献类型：
--
作者：
Burge, Johannes;Geisler, Wilson S.
散焦模糊几乎总是存在于自然图像中：只有一个距离的物体可以完美聚焦。在其他距离处的物体的图像被模糊取决于瞳孔直径和透镜属性的量。尽管散焦在行为、感知和生物学上都具有重要意义，但生物系统如何估计散焦还是未知的。给定一组自然场景和视觉系统的属性，我们从第一原理展示如何最佳地估计在任何单个图像中的每个位置处的散焦。我们表明，人类视觉系统的高精度，无偏估计可在自然观看条件下的补丁与可检测的对比度。考虑到自然图像的异质性，估计的高质量令人惊讶。此外，我们量化的程度，通常归因于散焦的符号模糊性解决的单色像差（散焦以外）和色差;色差完全解决的符号模糊性。最后，我们表明，简单的空间和空间色感受野提取的信息最佳。该方法可以针对任何环境视觉系统配对：自然或人造，动物或机器。因此，它提供了一个原则性的一般框架，用于分析整个动物王国的物种中的散焦估计的心理物理学和神经生理学，并为计算视觉系统开发最佳的基于图像的散焦和深度估计算法。
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 diameter 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 monochromatic 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 extract the information optimally. The approach can be tailored to any environment-vision system pairing: natural orman-made, animal or machine. Thus, it provides a principled general framework for analyzing the psychophysics and neurophysiology of defocus estimation in species across the animal kingdom and for developing optimal image-based defocus and depth estimation algorithms for computational vision systems.