Application of wavelet threshold function optimization algorithm in metal parts

Aiming at the discontinuity of the traditional hard threshold function at the threshold and the constant deviation between the original wavelet coefficient and the wavelet estimation coefficient in the soft function method, this paper proposed an image denoising algorithm based on the improved threshold function. This algorithm combined the advantages of the im-proved threshold function, Not only the high-frequency noise is effectively denoised, but the low-frequency components are also denoised appropriately, thereby improving the approximation of the reconstructed image and the original image. thereby improving the degree of approximation of the reconstructed image and the original image. The simulation results show that, compared with the traditional soft and hard threshold functions, using the optimized threshold function in this paper for image denoising, not only the subjective visual effect is better, but also the peak signal to noise ratio (PSNR) value increased by about 7db, structural similarity (Structural Similarity, SSIM) increased by about 0.1, and mean square error (Mean Square Error, MSE) was reduced about 76%. In addition, after the threshold function denoising and weighted mean filtering algorithm are used to denoise the image with salt and pepper noise, the simulation result is compared with the single threshold function denoising, and the peak signal to noise ratio increases by about 5%, the mean square error is reduced by about 5.5%, and the structural similarity is increased by about 0.1.