A Simple, General Model for the Affine Self-similarity of Images
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Effective band-limited extrapolation relying on Slepian series and l1 regularization
Computers & Mathematics with Applications
Self-similarity of images in the wavelet domain in terms of ℓ2 and structural similarity (SSIM)
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
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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