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Image denoising using total least squares.

Keigo Hirakawa1, Thomas W Parks

  • 1Cornell University, Ithaca, NY 14853, USA. hirakawa@stat.harvard.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 5, 2006
PubMed
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This study introduces a novel method for digital image denoising, effectively removing additive, multiplicative, and mixed noise. The technique utilizes total least squares (TLS) for robust noise reduction, enhancing image clarity and preserving edges.

Area of Science:

  • Digital Image Processing
  • Computer Vision
  • Signal Processing

Background:

  • Digital images are often corrupted by various noise types, including additive, multiplicative, and mixed noise.
  • Existing denoising methods may struggle with complex noise patterns or introduce artifacts.
  • Accurate noise removal is crucial for subsequent image analysis and interpretation.

Purpose of the Study:

  • To develop an effective method for removing diverse noise types from digital images.
  • To leverage the total least squares (TLS) framework for robust image denoising.
  • To enhance image quality by sharpening edges and minimizing artifacts.

Main Methods:

  • Modeling image patches from an ideal image as linear combinations of noisy image patches.

Related Experiment Videos

  • Applying the total least squares (TLS) fitting to account for data uncertainties.
  • Developing a patch-selection strategy to reduce the influence of irrelevant patches and preserve image details.

    • Main Results:

    • The proposed method successfully removes additive, multiplicative, and mixed noise from digital images.
    • The TLS approach effectively handles uncertainties in the measured image data.
    • The algorithm demonstrates improved edge preservation and reduced artifact generation compared to conventional methods.

    Conclusions:

    • The developed image denoising method using total least squares (TLS) is effective for various noise types.
    • The technique offers a robust solution for enhancing image quality, particularly in preserving fine details and edges.
    • Despite computational demands, the algorithm's performance validates the utility of TLS in image denoising applications.