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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
Summary
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.
- 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.
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