Аналіз даних атомно-силової мікроскопії

Abstract

Atomic force microscopy (AFM) is a powerful imaging tool that enables high-resolution imaging of surfaces and materials on the nanoscale. However, due to various factors such as instrument noise, sample drift, and mechanical vibrations, AFM images can be corrupted and difficult to interpret. In this thesis, we present novel image reconstruction methods that can enhance the quality and accuracy of AFM images. We first provide a comprehensive review of existing AFM image reconstruction techniques and their limitations. We then propose new algorithms that address some of these limitations, including the use of deep learning approaches and Bayesian inference. Overall, this thesis contributes to the development of advanced image reconstruction techniques in AFM and opens up new avenues for research in this field