Gaussian noise approximation for disparity-based autofocus
An analytical characterization of the accuracy of disparity-based autofocus requires an effective error model of disparity estimation. In this paper, we investigate the approximation of photon shot noise of images by a Gaussian model for disparity error analysis that takes defocus blur and image noise into account. We show that, counterintuitively, defocus blur alone does not affect the disparity estimation. However, its presence makes disparity estimation more vulnerable to image noise. In addition, we show that the Gaussian model is a fair approximation of the photon shot noise for disparity-based autofocus. The Gaussian model allows defocus blur to be mathematically separable from noise and facilitates further analysis of disparity error for disparity-based autofocus.