A Counterexample in Image Registration
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Abstract (arXiv)
Image registration is a widespread problem which applies models about image transformation or image similarity to align discrete images of the same scene. Nevertheless, the theoretical limits on its accuracy are not understood even in the case of one-dimensional data. Just as Nyquist's sampling theorem states conditions for the perfect reconstruction of signals from samples, there are bounds to the quality of reproductions of quantized functions from sets of ideal, noiseless samples in the absence of additional assumptions. In this work we estimate spatially-limited piecewise constant signals from two or more sets of noiseless sampling patterns. We mainly focus on the energy of the error function and find that the uncertainties of the positions of the discontinuity points of the function depend on the discontinuity point selected as the reference point of the signal. As a consequence, the accuracy of the estimate of the signal depends on the reference point of that signal.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 1 of its 4 references that are in the map. First posted on arXiv 14 Oct 2024.
Builds on
1 of 4 references.
References · 1
- 2024On Image Registration and Subpixel Estimationcited by 4
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Citing papers per year: 2024: 2; 2025: 1.
3 papers.
- 2024A Counterexample in Cross-Correlation Template Matchingcited by 2
- 2024On Round-Off Errors and Gaussian Blur in Superresolution and in Image Registrationcited by 1
- 2025On the Logic Elements Associated with Round-Off Errors and Gaussian Blur in Image Registration: A Simple Case of Comminglingcited by 0
- 2025On the Logic Elements Associated with Round-Off Errors and Gaussian Blur in Image Registration: A Simple Case of Commingling17 Feb 2025
- 2024On Round-Off Errors and Gaussian Blur in Superresolution and in Image Registration12 Dec 2024
- 2024A Counterexample in Cross-Correlation Template Matching24 Oct 2024
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