Cutting tool image denoising based on NSST and fast non-local means filter

To avoid the influence of the image noise in cutting tool wear condition monitoring system based on image processing, a cutting tool image denoising method based on non-subsampled shearlet transform (NSST) and fast non-local means(FNLM) filter is proposed. Firstly, the decision based unsymmetrical trimmed median(DBUTM) filter is applied to eliminate the pepper and salt noise in the original image. Then the image is decomposed by NSST into a low-frequency component and a series of high-frequency components. Finally, the FNLM filter and the anisotropic diffusion model are introduced to process the low-frequency component and high-frequency components, respectively, after which the denoised image is reconstructed with those modified coefficients of frequency subbands. The experimental results demonstrate that, compared with wavelet based threshold shrink method, contourlet based method combining total invariance model with anisotropic diffusion, shearlet based standard non-local means filtering method, the proposed method has a better performance in four aspects such as subjective visual denoising effect, peak signal to noise ratio, structural similarity and running speed.