u-net CNN based fourier ptychography
This work addresses a bottleneck in imaging for microscopy applications, offering an incremental improvement over existing iterative methods.
The paper tackles the slow and aberration-sensitive reconstruction in Fourier ptychography by proposing a convolutional neural network-based algorithm, which achieves faster and more robust high-quality image reconstruction.
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.