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Normalized weighted cross correlation for multi-channel image registration.

Gastón A Ayubi1, Bartlomiej Kowalski1, Alfredo Dubra1

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This study introduces a generalized normalized weighted cross-correlation (NWCC) for image registration, allowing pixel weighting for improved accuracy. The new method enhances registration of images with irregular sampling and boundaries.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Normalized Cross-Correlation (NCC) is a standard feature-agnostic method for image registration.
  • Existing NCC methods have limitations with images of arbitrary dimensions, channels, and sampling irregularities.

Purpose of the Study:

  • To generalize Normalized Cross-Correlation (NCC) by introducing pixel-wise weighting for enhanced image registration.
  • To develop a generalized Normalized Weighted Cross-Correlation (NWCC) and its zero-mean variant (ZNWCC).
  • To provide Discrete Fourier Transform (DFT) formulations for efficient computation of NWCC and ZNWCC.

Main Methods:

  • Reviewed existing NCC definitions across various image dimensions and channels.
  • Proposed a generalized NWCC allowing individual weighting of pixel values per channel.
  • Developed DFT formulations for fast computation of NWCC and ZNWCC, including overlap calculation.

Main Results:

  • Introduced generalized NWCC and ZNWCC enabling prioritization of pixels, e.g., by signal-to-noise ratio.
  • Demonstrated that NWCC facilitates registration of uniformly sampled images with irregular boundaries and sparse sampling.
  • Provided DFT-based computation methods for efficient implementation and overlap calculation.

Conclusions:

  • The proposed generalized NWCC and ZNWCC offer a flexible and powerful approach to image registration.
  • The DFT formulations ensure computational efficiency for practical applications.
  • The enhanced methods improve the registration of challenging image datasets with non-uniform sampling.