Signal processing from modern microscopes for local characteristics of materials Image processing is more and more important for the advancement of image evaluation taken from microscopes. This thesis engages the problem of artefact detection and removal from images taken by electron microscope, more accurately by low energy electron microscopy (LEEM). It then offers a possible course of processing such images by edge detection and its theoretical use. These operations are all made in MatLAB language.

Gyros Protein Technologies develops instruments for automated immunoassays. Fluorescent antibodies are added to samples and excited with a laser. This results in a 16-bit image where the intensity is correlated to concentration of bound antibody. Artefacts may appear on the images due to dust, fibers or other problems, which affect the quantification. This project seeks to automatically detect such artifacts by classifying the images as good or bad using Deep Convolutional Neural Networks (DCNNs). To augment the dataset a simulation approach is used and a simulation program is developed that generates images based on developed simulation models. Several classification models are tested as well as different techniques used for training. The highest performing classifier is a VGG16 DCNN, pre-trained on simulated images, which reaches 94.8% accuracy. There are many sub-classes in the bad class, and many of these are very underrepresented in both the training and test datasets. This means that not much can be said of the classification power of these sub-classes. The conclusion is therefore that until more of this rare data can be collected, focus should lie on classifying the other more common examples. Using the approaches from this project, we believe this could result in a high performing product.