Wavelet and Curvelet based Thresholding Techniques for Image Denoising

Abstract

In this paper an adaptive thresholding methods for removing additive white Gaussian noise from digital images are introduced. Some of the denoising algorithms perform thresholding of the wavelet coefficients, which have been affected by additive white Gaussian noise, by retaining only large coefficients and setting the rest to zero. However, their performance is not sufficiently effective as they are not spatially adaptive. But Curvelet are a non-adaptive technique for multi-scale object representation. Curvelet transform employed in the proposed scheme provides sparse decomposition as compared to the wavelet transform methods which being non geometrical lack sparsely and fail to show optimal rate of convergence. The proposed algorithm succeeded in providing improved denoising performance to recover the shape of edges and important detailed components. Simulation results proved that the proposed method can obtain a better image estimate than the wavelet based restoration methods.