A Novel Algorithm of Fractal-Wavelet Image Denosing

Abstract

Image denosing is the first preprocessing step in dealing with image processing where the overall system quality should be improved. So it is a key issue in all image processing researches. Over the past years, fractal-wavelet transforms were introduced in an effort to reduce the blockiness and computational complexity that are inherent in fractal image compression. The essence of fractal image denosing is to predict fractal code of a noiseless image from its noisy observation. From the predicted fractal code, we can generate an estimate of the original image. In the paper, we show how well fractal-wavelet denosing predicts parent wavelet subtrees of the noiseless image. The performance of various fractal-wavelet denosing schemes is compared to that of some standard wavelet thresholding methods. From the several of experimental results, these fractal-based image denosing methods are quite competitive with standard wavelet thresholding methods for image denosing.