Kalman filtering optimization in Digital Holographic Microscopy (DHM)

Digital Holographic Microscopy (DHM) can obtain a 3D profile of a micro-object. However, the obtained 3D profile includes the noise. One reason for this is the problem of the extraction method of phase information in DHM. Conventional methods cannot extract only the phase information from the Fourier domain, and noise components are included. To solve this problem, we propose a use of the Kalman filter in DHM systems. In this method, multiple frequency components are obtained from the Fourier domain as time-series data for Kalman filtering. 3D profiles are obtained from each frequency component and stored in an array as time-series data. This is used to remove randomly generated noise. In this process, it remains unclear how many 3D profiles obtained from frequency components are needed as time-series data. Therefore, the optimization of the number of windowed sidebands in the Fourier domain is the objective in this paper. The objective is to use the Kalman filter efficiently by performing quantitative evaluation using image quality metrics and optimization based on processing time.