Image Denoising Method of Spectrum Clustering Based on Non-local Similarity
The conventional image denoising algorithms just make use of the prior information of the natural image or the noise image alone,without effective combination of the prior imformation of two images to realize the image denoising.For this problem,a novel image denosing method which joins the prior information of the natural image and the non-local similarity of the noise image was proposed in this paper.Firstly,the similar blocks in natural image are clustered in the same class by the spectrum clustering,and the result of the spectrum clustering with the natural image is used to get the clustering of the noise image blocks.Then,the gotten same class blocks of the noise image are vectorized as a low-rank matrix.Secondly,the low-rank approximation process is adopted on the matrix to estimate the relative original image data.Finally,the original image can be reconstructed by the estimated image data.The experimental results show that compared with the RNL(adaptive regularization of the NL-Means) and LPG-PCA(two-stage image denoising by principal component analysis with local pixel grouping),the proposed algorithm can provide significant performance improvement with respect to both PSNR and local information preservation,which produces better denoising effect.
NETL
NSTL
万方数据
维普
