# On Image Registration and Subpixel Estimation

arXiv: https://arxiv.org/abs/2405.12927 · HTML: https://mlsanity.com/paper/2405.12927/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 13 references that are in the map.
- First posted on arXiv 21 May 2024.

## Built on by (most cited first, 4 of 4)

- [A Counterexample in Image Registration](https://mlsanity.com/paper/2410.10725/) (2024)
- [A Counterexample in Cross-Correlation Template Matching](https://mlsanity.com/paper/2410.19085/) (2024)
- [On Round-Off Errors and Gaussian Blur in Superresolution and in Image Registration](https://mlsanity.com/paper/2412.09741/) (2024)
- [On the Logic Elements Associated with Round-Off Errors and Gaussian Blur in Image Registration: A Simple Case of Commingling](https://mlsanity.com/paper/2502.11992/) (2025)

## Abstract (arXiv)

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.