The PCA Algorithm in Digital Holographic Microscopy under Structured Illumination

Principal component analysis (PCA) is a method of processing high-dimensional feature data, which can be decomposed into a set of unrelated variables called principal components. Digital holographic microscopy (DHM) is a powerful tool in the biomedical imaging for recording the amplitude and phase information of object simultaneously. Structured illumination (SI) has been introduced in DHM to improve the resolution, by which the resolution can be doubled. However, accurate phase-shifting is required to retrieve the low and high frequency information. Besides that, the aberration of imaging system makes DHM under SI cumbersome. This paper presents an algorithm based on PCA for DHM under SI. The aberration terms can be extracted from the first principal component of the exponential term of filtered hologram. Moreover, the low and high frequency information can be achieved from three images without prior knowledge of phase shift values. In synthesizing process, the spectrums are precisely shifted to the correct position in the spatial-frequency domain also based on the PCA. This paper verifies the feasibility of the PCA algorithm the experiment. It is an attractive and promising technology for DHM under SI.