The denoising of a natural image corrupted by Gaussian noise is a classical problem in image processing. Chen et al. have proposed an effective wavelet thresholding scheme using incorporating neighboring coefficients

namely NeighShrink. NeighShrink’s disadvantages are to use a suboptimal universal threshold and fixed neighboring window size for every subband. In this paper

we improve NeighShrink using Stein’s unbiased risk estimation(SURE). Our method can determine an optimal threshold and neighboring window size for every subband. Experimental results show that our proposed method is significantly better than NeighShrink in all test examples. It also outperforms Balster et al’s FeatShrink which is a recent sophisticated image denoising algorithm.