Advanced background elimination in digital holographic microscopy

Abstract

Background estimation and elimination is an indispensable step of hologram processing. Its application ensures that the fix pattern noise caused by the deposits, dirt and other impurities of the measuring chamber and the optical system do not contaminate the reconstructed holograms and improves the efficiency of the object segmentation. It is conventionally solved by averaging large number of holograms with altering objects within the flow-through cell. Due to the possible illumination changes the background should be updated incessantly during the hologram measuring process. Here we introduce an improved background estimation method where the holographic contributions of the segmented and reconstructed objects are excluded from the running average. The applied segmentation is based on the 3D positions of the objects within the flow-through measuring chamber. Therefore the objects can be distinguished from the impurities and deposits, which customary located at the walls of the measuring chamber. This way, an elevated speed, more adaptive background estimation becomes achievable with reduced noise. The applied object segmentation and hologram subtraction methods are presented also. To accelerate the processing of the measured holograms the application of some parallel computing implementation seems essential. Using stream processors (GPU) we were able to increase the algorithm speed considerably, without perceptible reconstruction accuracy loss.