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Deep learning-based complex hologram compression enhanced by ringing reduction

delete2026-01-15
delete0
PRE
AI
M
Makoto Sekiguchi *
C
Chung-Hsuan Huang
D
David Blinder
Y
Yabuki, Shosei
F
Fan Wang
P
Peter Schelkens
H
Han-Yen Tu
C
Chau-Jern Cheng
T
Tomoyoshi Ito
T
Tomoyoshi Shimobaba
DOI：10.1364/OPTCON.581618delete
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摘要

摘要

En 中文
计算机生成全息术（CGH）的最新进展通过高分辨率渲染显著提高了视觉质量；然而，伴随的数据量增加已成为实际部署的关键障碍。传统的图像压缩技术（如JPEG和高效率视频编码（HEVC））未能充分考虑到全息图的独特统计和光谱特性，从而限制了压缩效率和重建质量。本研究提出了一种针对数字全息图的专用高效压缩框架，通过将衍射计算中的振铃抑制技术与基于深度学习的图像压缩框架CompressAI相结合。我们证明振铃伪影是阻碍高效全息图压缩的主要因素，并表明神经网络可以通过学习全息图固有的统计和光谱特征实现更优性能。与常规方法相比，本方法在压缩效率与重建质量之间实现了更优的平衡，特别是在低比特率下。此外，通过引入多通道输入表示，本方法实现了更高的压缩比。（c）2026 Optica Publishing Group，根据Optica开放获取出版协议。
Keyword:
RECONSTRUCTION

期刊

O
Optics Continuum
IF:
1.4
论文数:
182
被引数:
0

机构

C
Chinese Culture University
学者数:
946
论文数: 1.2K
被引数: 1.3K
N
national taiwan normal university
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879
论文数: 508
被引数: 0
C
chiba university
学者数:
2.9K
论文数: 959
被引数: 0
V
vrije universiteit brussel
学者数:
2.0K
论文数: 906
被引数: 0
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