  Fourier ptychography is a recently explored imaging method for overcoming the
diffraction limit of conventional cameras with applications in microscopy and
yielding high-resolution images. In order to splice together low-resolution
images taken under different illumination angles of coherent light source, an
iterative phase retrieval algorithm is adopted. However, the reconstruction
procedure is slow and needs a good many of overlap in the Fourier domain for
the continuous recorded low-resolution images and is also worse under system
aberrations such as noise or random update sequence. In this paper, we propose
a new retrieval algorithm that is based on convolutional neural networks. Once
well trained, our model can perform high-quality reconstruction rapidly by
using the graphics processing unit. The experiments demonstrate that our model
achieves better reconstruction results and is more robust under system
aberrations.
