  Quantitative phase imaging (QPI) has been widely applied in characterizing
cells and tissues. Spatial light interference microscopy (SLIM) is a highly
sensitive QPI method, due to its partially coherent illumination and common
path interferometry geometry. However, its acquisition rate is limited because
of the four-frame phase-shifting scheme. On the other hand, off-axis methods
like diffraction phase microscopy (DPM), allows for single-shot QPI. However,
the laser-based DPM system is plagued by spatial noise due to speckles and
multiple reflections. In a parallel development, deep learning was proven
valuable in the field of bioimaging, especially due to its ability to translate
one form of contrast into another. Here, we propose using deep learning to
produce synthetic, SLIM-quality, high-sensitivity phase maps from DPM,
single-shot images as input. We used an inverted microscope with its two ports
connected to the DPM and SLIM modules, such that we have access to the two
types of images on the same field of view. We constructed a deep learning model
based on U-net and trained on over 1,000 pairs of DPM and SLIM images. The
model learned to remove the speckles in laser DPM and overcame the background
phase noise in both the test set and new data. Furthermore, we implemented the
neural network inference into the live acquisition software, which now allows a
DPM user to observe in real-time an extremely low-noise phase image. We
demonstrated this principle of computational interference microscopy (CIM)
imaging using blood smears, as they contain both erythrocytes and leukocytes,
in static and dynamic conditions.
