Geisler Wilson S, Perry Jeffrey S
Center for Perceptual Systems, University of Texas at Austin, Austin, TX 78712, USA.
J Vis. 2011 Oct 19;11(12):14. doi: 10.1167/11.12.14.
Sensory systems exploit the statistical regularities of natural signals, and thus, a fundamental goal for understanding biological sensory systems, and creating artificial sensory systems, is to characterize the statistical structure of natural signals. Here, we use a simple conditional moment method to measure natural image statistics relevant for three fundamental visual tasks: (i) estimation of missing or occluded image points, (ii) estimation of a high-resolution image from a low-resolution image ("super resolution"), and (iii) estimation of a missing color channel. We use the conditional moment approach because it makes minimal invariance assumptions, can be applied to arbitrarily large sets of training data, and provides (given sufficient training data) the Bayes optimal estimators. The measurements reveal complex but systematic statistical regularities that can be exploited to substantially improve performance in the three tasks over what is possible with some standard image processing methods. Thus, it is likely that these statistics are exploited by the human visual system.
感觉系统利用自然信号的统计规律，因此，理解生物感觉系统以及创建人工感觉系统的一个基本目标是刻画自然信号的统计结构。在此，我们使用一种简单的条件矩方法来测量与三个基本视觉任务相关的自然图像统计量：（i）缺失或遮挡图像点的估计，（ii）从低分辨率图像估计高分辨率图像（“超分辨率”），以及（iii）缺失颜色通道的估计。我们采用条件矩方法是因为它做出的不变性假设最少，可以应用于任意大的训练数据集，并且（在给定足够训练数据的情况下）提供贝叶斯最优估计器。这些测量揭示了复杂但系统的统计规律，利用这些规律可以在这三个任务中显著提高性能，超过一些标准图像处理方法所能达到的水平。因此，人类视觉系统很可能利用了这些统计量。
