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Fractal-wavelet image denoising revisited

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3 Author(s)
Ghazel, M. ; Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont. ; Freeman, G.H. ; Vrscay, E.R.

The essence of fractal image denoising is to predict the fractal code of a noiseless image from its noisy observation. From the predicted fractal code, one can generate an estimate of the original image. We show how well fractal-wavelet denoising predicts parent wavelet subetres of the noiseless image. The performance of various fractal-wavelet denoising schemes (e.g., fixed partitioning, quadtree partitioning) is compared to that of some standard wavelet thresholding methods. We also examine the use of cycle spinning in fractal-based image denoising for the purpose enhancing the denoised estimates. Our experimental results show that these fractal-based image denoising methods are quite competitive with standard wavelet thresholding methods for image denoising. Finally, we compare the performance of the pixel- and wavelet-based fractal denoising schemes

Published in:

Image Processing, IEEE Transactions on  (Volume:15 ,  Issue: 9 )