Combined Curvelet and Wavelet Denoising

The purpose of this paper is to develop a method for denoising images corrupted with additive white Gaussian noise (AWGN). The noise degrades quality of the images and makes interpretations, analysis and segmentation of images harder. The discrete curvelet transform is a new image representation approach that codes image edges more efficiently than the wavelet transform. On the other hand, wavelet transform codes homogeneous areas better than curvelet transform. In this paper an adaptive combined method (ACM), which uses the undecimated discrete wavelet transform (UDWT) to denoise homogeneous areas and the fast discrete curvelet transform (FDCT) to denoise areas with edges is proposed