**Shuming Jiao 1,2,3,†, Zhi Jin 1,2,†, Chenliang Chang 4, Changyuan Zhou 1,2, Wenbin Zou 1,2,\* and Xia Li <sup>1</sup>**


Received: 22 June 2018; Accepted: 24 July 2018; Published: 30 July 2018

**Abstract:** 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.

**Keywords:** hologram; holography; phase-only; compression; deep learning; JPEG; convolutional neural network
