On Image Registration and Subpixel Estimation

TL;DR

This paper investigates the fundamental limits of subpixel image registration accuracy in a simplified one-dimensional setting, highlighting how function complexity, pixel size, and sample count influence estimation precision.

Contribution

It introduces an idealized model to analyze the factors affecting subpixel estimation, emphasizing the role of function complexity and sampling in measurement accuracy.

Findings

Estimation accuracy depends on the complexity of the underlying function.

The relationship between the function and pixel size affects subpixel inference.

Sample size influences the potential for subpixel estimation.

Abstract

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.