关键词: 先优化, 后分类, 图像去噪, 最小均方误差

Abstract: An improved image denoising method of first optimization and last classification in the wavelet domain is proposed.It is an improvement of the existing denoising method NeighShrink.The proposed method determines an optimal threshold and the window of the neighborhood using stein unbiased risk estimation in the wavelet domain for each sub-band.According to neighborhood threshold size，it divides wavelet coefficients of sub-band into“small”coefficients or“big”coefficients.Those“small”coefficients are set to zero，whereas those “big” coefficients are modeled as zero-mean Ganssian random variables with high local correlation and the estimation of the true coefficients are obtained by minimum mean squared error criterion.The experimental results show that the proposed method obviously outperforms the NeighShrink method in the peak signal to noise ratio.At the same time，it effectively preserves image texture information，and has better visual effects.

Key words: first optimization, last classification, image denoising, minimum mean squared error