Image Denoising Based on Anisotropic Diffusion and Sparse Representation in Shearlet Domain
WU Yi-qua · 2014
To suppress image noise effectively and better preserve edge details,an image denoising method based on anisotropic diffusion and sparse representation in the shearlet domain is proposed.The noisy image is first decomposed into a low frequency component and several high frequency components by non-subsampled shearlet transform(NSST).The main energy of the image information is contained in the low frequency component,while the edge information and most of noise are contained in high frequency components.The K-singular value decomposition(K-SVD) algorithm is used to remove noise in low frequency component.The kernel anisotropic diffusion(KAD) algorithm is used to reduce noise in each high frequency component.The reconstructed image is obtained by inverse non-subsampled shearlet transform(INSST) for the processed low frequency and high frequency components.Noise in the image is effectively suppressed,and edge details are preserved satisfactorily.Experimental results show that,compared with the denoising methods such as wavelet combining with nonlinear diffusion method,shearlet hard threshold method,K-SVD sparse denoising method and sparse redundant denoising method in wavelet domain,the proposed method has better performance both in noise reduction and detail preservation.