Abstract: For the conventional wavelet soft threshold denoising method, the wavelet coefficients of the image before and after processing are different, resulting in serious image distortion after de- noising. In order to further improve the denoising effect and improve the ability of denoising and detail preserving, the threshold selection method and the threshold function are improved. The improved method determines the threshold value through the length of each sub-band of wavelet transform to realize the adaptive and accurate quantification of the threshold value. The improved soft threshold function uses the hyperbolic tangent function to replace the symbol function, and gradually compresses the wavelet coefficients within the absolute value range of the threshold by nonlinear function, so that the improved threshold function has better continuity and stronger stability. The simulation results show that the peak signal-to-noise ratio (PSNR) and structure similarity of the improved wavelet threshold denoising method are improved by 48% and 80.6% respectively. It is concluded that, compared with the conventional wavelet threshold denoising method, the new improved wavelet soft threshold denoising method can effectively suppress the noise while retaining the details of the original image, and the image qua- lity is significantly improved.

Key words: wavelet transform, threshold function, peak signal-to-noise ratio, structural similarity, noise, preserve details