We develop advanced image processing algorithm to enhance signal-to-noise ratio for high-precision measurements.
Through our thorough understanding of the optical microscope system and proper design of the experiment, we are able to suppress the noise, enhance the signal, and analyze the data in a digital manner. These data analysis and processing not only make the measurement more quantitative and scientific, they also recover the signal that is originally hard to perceive in the raw data.

We develop a novel image processing that extracts the dynamic signal from a massive, relatively static background. This method is especially powerful in scattering-based imaging because the signal of a small particle is usually overwhelmed by the strong background. We find that in ultrahigh-speed imaging, the background is relatively stationary compared to the signal. By using our method, the signal-to-noise ratio is greatly improved, making it possible to detect and to track small particles in live cellular environments.