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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
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Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
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Interpolation artifacts in sub-pixel image registration.

Gustavo K Rohde1, Akram Aldroubi, Dennis M Healy

  • 1Center for Bioimage Informatics, Biomedical Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. gustavor@cmu.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 2009
PubMed
Summary
This summary is machine-generated.

Image registration to sub-pixel accuracy can be improved by addressing local optima introduced by interpolation. Solutions include B-spline interpolation, low-pass filtering, and stochastic integration for robust image alignment.

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Area of Science:

  • Medical image analysis
  • Computer vision
  • Computational imaging

Background:

  • Accurate image registration is crucial for comparing and analyzing medical images.
  • Optimization-based registration methods rely on objective functions derived from image intensities.
  • Common objective functions include sum of squared differences, cross-correlation, and mutual information.

Purpose of the Study:

  • To investigate the impact of interpolation methods on the optimization landscape of image registration.
  • To identify and mitigate the occurrence of local optima in image registration objective functions.
  • To propose and evaluate robust solutions for achieving sub-pixel accuracy in image registration.

Main Methods:

  • Analysis of objective functions for image registration.
  • Investigation of interpolation artifacts, specifically local optima.
  • Implementation of B-spline interpolation with high degrees.
  • Application of low-pass filtering techniques.
  • Utilizing stochastic integration for optimization.
  • Validation with synthetic and real image datasets.

Main Results:

  • Widely used interpolation methods can introduce problematic local optima in registration objective functions.
  • These local optima can hinder the convergence of optimization algorithms, leading to inaccurate alignment.
  • Proposed solutions, including advanced B-spline interpolation and filtering, effectively reduce or eliminate local optima.
  • Demonstrated successful sub-pixel registration accuracy on both synthetic and real-world image data.

Conclusions:

  • Interpolation choice significantly impacts the success of optimization-based image registration.
  • Careful selection and application of interpolation and optimization strategies are essential for achieving high-accuracy image alignment.
  • The presented methods offer robust solutions for overcoming local optima challenges in image registration.