  Lensfree on-chip microscopy is an emerging imaging technique that can be used
to visualize and study biological specimens without the need for imaging lens
systems. Important issues that can limit the performance of lensfree on-chip
microscopy include interferometric aberrations, acquisition noise, and image
reconstruction artifacts. In this study, we introduce a Bayesian-based method
for performing aberration correction and numerical diffraction that accounts
for all three of these issues to improve the effective numerical aperture (NA)
and signal-to-noise ratio (SNR) of the reconstructed microscopic image. The
proposed method was experimentally validated using the USAF resolution target
as well as real waterborne Anabaena flos-aquae samples, demonstrating
improvements in NA by ~25% over the standard method, and improvements in SNR of
2.3 dB and 3.8 dB in the reconstructed image when compared to the reconstructed
images produced using the standard method and a maximum likelihood estimation
method, respectively.
