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Hashed nonlocal means for rapid image filtering.

Nicholas Dowson1, Olivier Salvado

  • 1Australian e-Health Research Centre, Royal Brisbane and Women’s Hospital, Herston, QLD, Australia. nicholas.dowson@csiro.au

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 10, 2010
PubMed
Summary
This summary is machine-generated.

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This study introduces a fast, hashed nonlocal means algorithm for image denoising. It significantly reduces computation time for 3D image filtering while maintaining high accuracy.

Area of Science:

  • Image Processing
  • Computer Vision
  • Computational Imaging

Background:

  • Image denoising is crucial for improving image quality and reducing acquisition time.
  • Nonlocal means (NLM) is effective but computationally expensive, especially for 3D images.
  • Existing NLM optimizations are often insufficient for practical 3D image filtering.

Purpose of the Study:

  • To develop a computationally efficient NLM algorithm for 3D image denoising.
  • To overcome the prohibitive computational cost of traditional NLM methods.
  • To achieve fast and accurate noise reduction in 3D images.

Main Methods:

  • Proposed a hashed approach using summed frequency functions of local image patches.
  • Discretized hash spaces on a regular grid, enabling linear operations.

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  • Utilized recursive hash spaces to manage memory requirements and marginal linear interpolation for speed.
  • Selected patch features for high computational efficiency and accuracy.

    • Main Results:

    • The proposed hashed NLM algorithm significantly reduces processing time for 3D images.
    • Achieved filtering times under one minute, compared to 15 minutes to 3 hours for existing NLM methods.
    • Maintained similar accuracy in noise removal and image structure preservation.

    Conclusions:

    • The hashed NLM approach offers a practical and efficient solution for 3D image denoising.
    • This method effectively balances noise reduction, image structure preservation, and computational speed.
    • Enables faster and more accessible high-quality 3D image processing.