Ramya An Efficient SVD Based Filtering For Image Denoising With Ridgelet Approach D

32 Abstract— Images are often contaminated by noise during the processes of acquisition and transmission. One of the fundamental challenges in the field of image processing and computer vision is image denoising, where the underlying goal is to estimate the original image by suppressing noise from a noise-contaminated version of the image. There are various existing methods to denoise image. The important property of a good image denoising model is that it should completely remove noise as far as possible as well as preserve edges. Discrete Cosine Transform(DCT), Discrete Wavelet Transform(DWT) ,Singular Value Decomposition( SVD), Ridgelet transform etc are used for denoising. Because of the lack of sparsity, edges cannot be coded or restored effectively using DCT.Wavelet transform fails to provide an adequate sparse representation for image containing complex singularities. SVD provide low computational complexity. But it requires some more methods for solving large images. Ridgelet transform is able to compete with the wavelet transform in current era of image restoration but slightly inferior in homogenous region in non textured images. Proposed method combines SVD and Ridgelet transform .Experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both PSNR and subjective visual quality.