Clustering Based Image Denoising Using SURE-LET

In this paper, we proposed a novel image denoising method based on clustering using SURE-LET. This method divides the images into several clusters and minimize the Steins unbiased risk estimator (SURE) of each cluster independently, which makes different clusters of pixels were denoised by different threshold functions in the image domain. The proposed method included the traditional SURE-LET as its special case, when the clusters reduced to one. Being more flexible, the proposed method results in smaller SURE and MSE. Experimental results show that the proposed method is effective.