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On The Performance of Bilateral Filters in Denoising of Machined Surface Images

K. Kannan, D. Palani Kumar


Surface roughness measurement by machine vision is gaining more importance now a day due to its accuracy, flexibility and low cost. For accurate surface roughness measurement, the image captured by the vision system should be free of noise. In this paper, the performance of bilateral filter is compared with other image denoising filters like Wiener filter, anisotropic filter in removing the additive white Gaussian noise from the machined surface images. The aim of this work is to apply standard, robust and weighted bilateral filters on milled surface images and grinded surface images for quantitative assessment. This paper also presents the effect of various range and domain parameter values in the performance of bilateral filters in removing the Gaussian noise from the machined surface images.

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