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Related Concept Videos

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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Related Experiment Video

Updated: Jul 7, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
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Published on: October 27, 2023

Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration.

Eun Sil Lee1, Moon Gi Kang

  • 1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
Summary

This study introduces a robust high-resolution image reconstruction algorithm that effectively handles inaccurate subpixel registration. The novel method adaptively estimates regularization parameters, improving image quality without prior information.

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Area of Science:

  • Image processing
  • Computer vision
  • Signal processing

Background:

  • Accurate image registration is crucial for high-resolution image reconstruction.
  • Subpixel registration errors can degrade reconstruction quality.
  • Multiframe environments present challenges due to varying registration errors.

Purpose of the Study:

  • To develop a high-resolution image reconstruction algorithm robust to inaccurate subpixel registration.
  • To overcome ill-posedness issues arising from registration inaccuracies.
  • To enable adaptive regularization parameter estimation for multiframe environments.

Main Methods:

  • A regularized iterative reconstruction algorithm is employed.
  • Multichannel image reconstruction techniques are utilized.
  • Adaptive regularization parameter estimation methods are proposed and validated.

Main Results:

  • The proposed algorithms demonstrate robustness against registration error noise.
  • No prior information about the original image or registration error is required.
  • Performance is superior to conventional methods in objective and visual evaluations.

Conclusions:

  • The developed algorithm significantly improves high-resolution image reconstruction quality.
  • Adaptive regularization parameter estimation enhances performance in multiframe scenarios.
  • The approach offers a practical solution for real-world image reconstruction challenges.