A novel approach to image calibration in super-resolution microscopy

For many disciplines in natural sciences like biology, chemistry or medicine, the invention of optical microscopy in the late 1800's provided groundbreaking insight into biomedical mechanisms that were not observable before with the unaided eye. However, the diffraction limit of the microscope gives a natural constraint on the image resolution since objects which are smaller than half the wavelength of the illuminating light - such as proteins or ions - cannot be recognised in classical microscopy. Recently, different techniques have been developed to partly overcome this restriction using fluorescent molecules as markers. Like this, it is possible to monitor a vast diversity of intracellular processes on a molecular level which are of interest for biomedical research. Since these developments in superresolution microscopy are quite recent, suitable data analysis techniques are still to be advanced. This work aims to deploy the potential of the so-called Hypothesised filter for Independent Stochastic Populations (HISP) for multi-object estimation in a biomedical context by extending its framework to a novel joint object state and sensor drift estimator.