Comparison Analysis of a New Color HOSVD & Varies De-Noising Filtering Technique for the ‘Lena’ Image Corrupted by Pepper & Salt Noise

In this work, we propose a very superior and elegant patch-based, machine learning technique for image de-noising using the higher order singular value decomposition (HOSVD). The method simply groups together similar patches from a noisy image (with similarity defined by a statistically motivated criterion) into a 3D stack, computes the HOSVD coefficients of this stack, manipulates these coefficients by hard thresholding, and inverts the HOSVD transform to produce the final denoised image. Our method chooses all required parameters in a principled way, relating them to the noise model. We also discuss our motivation for adopting the HOSVD as an appropriate transform for image de-noising. We practically demonstrate the better performance of the method on color images. On color images, our method produces state-of-the-art results, outperforming other color image de-noising algorithms at moderately high noise levels. A criterion for optimal patchsize selection and noise variance estimation from the residual images (after de-noising) is also presented. And also using varies filter VMF, VDF, DDF .our main Moto is comparing the both method HOSVD and varies de-noising method. Keyword-Image De-Noising, Singular Value Decomposition (SVD), Higher Order Singular Value Decomposition (HOSVD), Varies De-Noising Method, Patch Similarity