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Related Experiment Videos

Fractal image denoising.

Mohsen Ghazel1, George H Freeman, Edward R Vrscay

  • 1Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada. mghazel@engmail.uwaterloo.ca

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
PubMed
Summary
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Fractal coding can enhance and restore noisy images by suppressing noise. A novel fractal-based method estimates the original image

Area of Science:

  • Digital Image Processing
  • Fractal Geometry
  • Signal Processing

Background:

  • Fractal coding is primarily used for image compression.
  • Applications in image enhancement and restoration are underexplored.
  • Noise significantly degrades image quality.

Purpose of the Study:

  • To propose and evaluate a fractal-based method for noisy image enhancement and restoration.
  • To investigate noise suppression capabilities of fractal coding.
  • To compare fractal-based restoration with existing methods like the Lee filter.

Main Methods:

  • Fractal coding of noisy images to suppress noise.
  • Estimating the fractal code of the noise-free image from the noisy image's code.
  • Utilizing knowledge of noise variance (zero-mean, stationary, Gaussian).

Related Experiment Videos

Main Results:

  • Direct fractal coding suppresses a significant amount of noise.
  • The proposed method yields enhanced and restored images.
  • Results are consistent with human visual perception, preserving high-frequency details.
  • Fractal-based scheme outperforms the Lee filter for significant noise (sigma >= 20).

Conclusions:

  • Fractal-based methods offer a powerful approach for image enhancement and restoration.
  • The proposed technique provides superior results compared to traditional filtering methods for noisy images.
  • Fractal coding has broader applications beyond image compression.