Enhanced lathe tool positioning through wavelet-based image denoising techniques

Precise tool placement is crucial in smart manufacturing and automation. In micro-machining, tiny measurements must be very accurate. Finding edges accurately is a key step for good positioning and micrometer-level measurements. Image denoising was necessary before applying edge detection algorithms to enhance edge detection accuracy. Conventional filtering methods can be employed for denoising, but the work here investigates wavelet thresholding benefits in analyzing multiple resolutions and achieving better accuracy. An in-depth assessment of the quality of the denoised images is conducted. The quantitative analysis is done using metrics such as PSNR (peak signal to noise ratio), SSIM (structural similarity index measurement), MSE (mean squar e error) and others factors in which the wavelet based denoising method's results are compared at different scales. The results from the analysis show that the db1 wavelet based on SURE denoising method can efficiently remove a lot of Gaussian noise and restore the image details and data than Coiflets and Symlets wavelet techniques.