A Study of Noise Reduction Algorithm Using Statistical Optimization in Digital Holographic Microscopy

Recently, due to the coronavirus era, we have a heightened need for fast and accurate diagnostics. To meet these requirements, there are many methods such as polymerase chain reaction, but they also require about from a few hours to a day. In contrast, a three-dimensional model of cells can be observed by simply extracting cells and photographing them with digital holographic microscopy, and in the case of diseases that can be classified b y t he shape of cells, diagnosis i s possible in a few minutes. However, there are precise focal issues and noise in the high-frequency domain, respectively. Therefore, to resolve these issues, we propose an optimization method by modifying the threshold value of the high variance pixel averaging method in digital holographic microscopy.