Improved image denoising method of first optimization and last classification in wavelet domain
Zhang Xi-huang · Computer Engineering and Applications Journal · 2011
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 intosmallcoefficients orbigcoefficients. Thosesmallcoefficients are set to zero,whereas thosebigcoefficients 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.