On the use of deep learning for phase recovery

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely,…

• NN
  National Natural Science Foundation of China

  Award: 61927810
• CU
  Chinese University of Hong Kong
• IA
  Innovation and Technology Commission - Hong Kong
• IA
  Innovation and Technology Fund

  Award: ITS/148/20