Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Fractal-wavelet image denoising revisited.

Mohsen Ghazel1, George H Freeman, Edward R Vrscay

  • 1Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada. mghazel@ece.ubc.ca

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 5, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Forecasting pulmonary air leak duration following lung surgery using transpleural airflow data from a digital pleural drainage device.

Journal of thoracic disease·2018
Same author

On the mathematical properties of the structural similarity index.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2011
Same author

Geometry-based image retrieval in binary image databases.

IEEE transactions on pattern analysis and machine intelligence·2008
Same author

Fractal image denoising.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Measuring information gain for frequency-encoded super-resolution MRI.

Magnetic resonance imaging·2007
Same author

Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour.

Physics in medicine and biology·2004
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Fractal image denoising effectively predicts image details using fractal codes. These fractal-wavelet methods compete well with standard wavelet denoising techniques.

Area of Science:

  • Digital Image Processing
  • Signal Processing
  • Computer Vision

Background:

  • Image denoising aims to restore a clean image from a corrupted observation.
  • Fractal-based methods offer a novel approach to image denoising by predicting fractal codes.
  • Wavelet transforms are widely used for image denoising, providing a benchmark for performance.

Purpose of the Study:

  • To evaluate the effectiveness of fractal-wavelet denoising schemes in predicting image components.
  • To compare the performance of different fractal-wavelet denoising methods against standard wavelet thresholding.
  • To investigate the impact of cycle spinning on fractal-based image denoising.

Main Methods:

  • Fractal image denoising using fractal code prediction.

Related Experiment Videos

  • Fractal-wavelet denoising with fixed and quadtree partitioning.
  • Application of cycle spinning to enhance denoised estimates.
  • Comparison with standard wavelet thresholding techniques.

    • Main Results:

    • Fractal-wavelet denoising accurately predicts parent wavelet subtrees.
    • Fractal-based methods demonstrate competitive performance against wavelet thresholding.
    • Cycle spinning improves denoised image quality in fractal-based schemes.
    • Pixel-based and wavelet-based fractal denoising schemes show varying performance.

    Conclusions:

    • Fractal-wavelet denoising is a viable and competitive alternative to traditional wavelet thresholding.
    • The choice of partitioning strategy and the use of cycle spinning influence denoising performance.
    • Further research can explore hybrid approaches combining fractal and wavelet techniques for optimal image restoration.