On Image Registration and Subpixel Estimation
This provides theoretical insights into a classical computer vision problem, but appears incremental as it focuses on an idealized one-dimensional case.
The paper tackles the fundamental problem of aligning discrete images to subpixel accuracy by analyzing a simplified one-dimensional model, showing that the feasibility of subpixel estimation depends on function complexity, pixel size relationships, and available sampling observations.
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.