Enhanced Resolution Methods for Improving Image Analysis and Pattern Recognition in Scanning Probe Microscopy

Image acquisition systems integrated with laboratory automation produces multi-dimensional datasets. An effective computational approach to objectively analyzing image datasets is pattern recognition (PR), i.e. a machine-learning approach where the machine finds relevant patterns that distinguish groups of objects after being trained on examples (supervised machine learning). In contrast, the other approach to machine learning and artificial intelligence is unsupervised learning, where the intelligent process finds relevant patterns without relying on prior training examples, usually by using a set of pre-defined rules. In this paper we apply a method derived by usual PR techniques for the recognition of artifacts and noise on images recorded with Atomic Force Microscopy (AFM). The advantage of automatic artifacts recognition could be the implementation of machine learning languages for AFM investigations.