Comparative Study of Image Denoising Using Wavelet Transforms and Optimal Threshold and Neighbouring Window

Abstract

NeighShrink is an efficient image denoising algorithm based on the discrete wavelet transform (DWT). Its disadvantage is to use a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. Dengwen and Wengang proposed an improved method, which can determine an optimal threshold and neighbouring window size for every subband by the Stein’s unbiased risk estimate (SURE). Its denoising performance is considerably superior to NeighShrink and also outperforms SURE-LET, which is an up-todate denoising algorithm based on the SURE. In this paper different wavelet transform families are used with this improved method, the results show that Haar wavelet has the lowest performance among other wavelet functions. The system was implemented using MATLAB R2010a. The average improvement in term of PSNR between Haar and other wavelet functions is 1.37dB