Noise Analysis to Guide Denoising of Scanning Electron Microscopy Images

Sheikh Shah Mohammad Motiur Rahman, Michel Salomon, Sounkalo Dembélé

Abstract: The paper deals with the analysis of noise in Scanning Electron Microscopy (SEM) images with respect to the dwell time, i.e. the time to acquire a pixel. Identifying the type and parameters of noise in images is crucial for denoising, as most denoising methods rely on prior knowledge of noise. Therefore, analyzing image noise is a significant undertaking and a window-wise based image segmentation of a single image has been proposed to find the smoothest region of the image. In fact, automating the identification of the flat region, noise type estimation, and statistical verification are essential aspects of this study. All the experiments were performed on a set of images obtained from a Zeiss Auriga field effect SEM and . It is shown that the noise types and levels change with respect to dwell time. The fast (dwell time 100–500 nanoseconds) and faster scan speed (dwell time less than 100 nanoseconds) changes the noise type to Gamma distribution, while for the slow one (few microseconds or more than 500 nanoseconds) it follows a Gaussian distribution. Moreover, the level of the noise are subject to higher values with fast and very fast scan speeds. The study suggests that utilizing its findings can establish a basis for prior knowledge in deep learning denoisers, which can assist in guiding the process of SEM image denoising.