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Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

التفاصيل البيبلوغرافية
العنوان: Compression of Phase-Only Holograms with JPEG Standard and Deep Learning
المؤلفون: Shuming Jiao, Zhi Jin, Chenliang Chang, Changyuan Zhou, Wenbin Zou, Xia Li
المصدر: Applied Sciences, Vol 8, Iss 8, p 1258 (2018)
بيانات النشر: MDPI AG, 2018.
سنة النشر: 2018
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: hologram, holography, phase-only, compression, deep learning, JPEG, convolutional neural network, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: http://www.mdpi.com/2076-3417/8/8/1258; https://doaj.org/toc/2076-3417
DOI: 10.3390/app8081258
URL الوصول: https://doaj.org/article/31c2d7db03494cff847091e8a2f0d9ef
رقم الأكسشن: edsdoj.31c2d7db03494cff847091e8a2f0d9ef
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app8081258