Image registration is a classical problem in machine vision which seeks methods to align discrete images of the same scene to subpixel accuracy in general situations. As with all estimation problems, the underlying difficulty is the partial information available about the ground truth. We consider a basic and idealized one-dimensional image registration problem motivated by questions about measurement and about quantization, and we demonstrate that the extent to which subinterval/subpixel inferences can be made in this setting depends on a type of complexity associated with the function of interest, the relationship between the function and the pixel size, and the number of distinct sampling count observations available.

翻译：图像配准是机器视觉中的经典问题，旨在寻求在一般情况下将同一场景的离散图像以亚像素精度对齐的方法。如同所有估计问题，其根本难点在于真实信息的部分可获取性。本文考虑一个基于测量与量化问题驱动的、基本且理想化的一维图像配准问题，并证明在此框架下，亚区间/亚像素推断的可行程度取决于目标函数的复杂度类型、函数与像素尺寸的关系，以及可获得的离散采样计数观测数量。