mlsanityData to 14 Oct 2026

A Counterexample in Image Registration

arXiv 2410.10725 ↗First posted 14 Oct 2024cs.CVcited by 3

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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

  1. 2024On Image Registration and Subpixel Estimationcited by 4

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Citing papers per year: 2024: 2; 2025: 1.

3 papers.

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  1. 2024Diff-Reg v1: Diffusion Matching Model for Registration Problem
  2. 2022Boomerang: Local sampling on image manifolds using diffusion models
  3. 2025Diff-Reg v2: Diffusion-Based Matching Matrix Estimation for Image Matching and 3D Registration
  4. 2026Asymptotic Preservation and Uniform Accuracy of Diffusion and Flow-Matching Samplers
  5. 2025Geometric Consistency Refinement for Single Image Novel View Synthesis via Test-Time Adaptation of Diffusion Models
  6. 2022Signal Reconstruction from Quantized Noisy Samples of the Discrete Fourier Transform
  7. 2026How Many Directions Must a Truncated Diffusion Sampler Retain? Matching Bounds Under Power-Law Spectra
  8. 2026Iterating Consistency Models: Stability, Error Bounds and Noise Schedules