  It is a critical issue to reduce the enormous amount of data in the
processing, storage and transmission of a hologram in digital format. In
photograph compression, the JPEG standard is commonly supported by almost every
system and device. It will be favorable if JPEG standard is applicable to
hologram compression, with advantages of universal compatibility. However, the
reconstructed image from a JPEG compressed hologram suffers from severe quality
degradation since some high frequency features in the hologram will be lost
during the compression process. In this work, we employ a deep convolutional
neural network to reduce the artifacts in a JPEG compressed hologram.
Simulation and experimental results reveal that our proposed "JPEG + deep
learning" hologram compression scheme can achieve satisfactory reconstruction
results for a computer-generated phase-only hologram after compression.
