title: Fractal-wavelet image denoising revisited.
abstract: 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 subtress 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.
authors.affiliation: Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada. mghazel@ece.ubc.ca
authors.forename: Mohsen
authors.initials: M
authors.lastname: Ghazel
authors.forename: George H
authors.initials: GH
authors.lastname: Freeman
authors.forename: Edward R
authors.initials: ER
authors.lastname: Vrscay
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D017709:Fractals; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D013269:Stochastic Processes