Dual-tree Complex Wavelets Transforms for Image Denoising
Bo Chen, Geng Zexun, Yang Yang, Shen Tianshuang · 2007
The ridgelet transform was developed over several years to break the limitations of the wavelet transform. In this paper, a novel image denoising algorithm is proposed that incorporates the dual-tree complex wavelets into the ordinary ridgelet transform. The approximate shift invariant property of the dual-tree complex wavelet and the high directional sensitivity of the ridgelet transform make the new method a very good choice for image denoising. We apply the digital complex ridgelet transform to the denoising of some standard images embedded in white noise. A simple hard thresholding of the complex ridgelet coefficients is used. Experimental results show that by using dual-tree complex ridgelets, our algorithms obtain higher peak signal to noise ratio (PSNR) for all the denoised images with different noise levels. The new modified ridgelet denoising algorithm - MRDA is better than Wiener2 and the classical CRD A ridgelet image denoising. Complex ridgelet could be applied to curvelet image denoising as well.