Machine Learning Aided Classification of Noise Distribution in Scanning Electron Microscopy Images

Noise estimation is a crucial part of any modern supervised denoiser. Various statistical approaches are studied to estimate the noise, but generally these depend on a manual analysis of the images. To remove the manual dependency, it is important to automate the noise estimation process. In this paper, the initial phase of noise estimation, which is to identify the types of noise distribution, is performed using machine learning (ML) techniques. To make the images workable with ML techniques, a feature extraction process was performed. Hu's moment invariants, Haralick's texture, and color histogram are extracted from the images and stacked horizontally by scaling with MinMax scale the three features into one. Label encoder is used for normalizing the labels. Multiple ML techniques are trained and validated, and then tested with unknown images. The result is that stacking multiple ML techniques can produce better results with an accuracy above 90%. Stacking with the test set produces the following scores for Precision, Recall, and F1-score: 0.98, 0.88, 0.93, and 0.89, 0.98, 0.93 for Gaussian and Poisson respectively, with an average precision of 88%. These promising results prove the capability of ML techniques for image noise classification tasks where noise is artificially added. However, in real case, i.e. when the images come from a Zeiss Auriga FE SEM, which is the initial target, the classification is not as efficient. Thus, it is not always possible to work in a real denoising scenario if the model is trained with synthetic data.