  Fourier ptychographic microscopy (FPM) is a recently proposed computational
imaging technique with both high resolution and wide field-of-view. In current
FP experimental setup, the dark-field images with high-angle illuminations are
easily submerged by stray light and background noise due to the low
signal-to-noise ratio, thus significantly degrading the reconstruction quality
and also imposing a major restriction on the synthetic numerical aperture (NA)
of the FP approach. To this end, an overall and systematic data preprocessing
scheme for noise removal from FP's raw dataset is provided, which involves
sampling analysis as well as underexposed/overexposed treatments, then followed
by the elimination of unknown stray light and suppression of inevitable
background noise, especially Gaussian noise and CCD dark current in our
experiments. The reported non-parametric scheme facilitates great enhancements
of the FP's performance, which has been demonstrated experimentally that the
benefits of noise removal by these methods far outweigh its defects of
concomitant signal loss. In addition, it could be flexibly cooperated with the
existing state-of-the-art algorithms, producing a stronger robustness of the FP
approach in various applications.
