全息术
全息显示器
计算机科学
光学
卷积(计算机科学)
人工智能
计算机视觉
数字全息术
匹配(统计)
图像质量
核(代数)
计算机图形学(图像)
图像(数学)
物理
人工神经网络
数学
统计
组合数学
作者
Koray Kavaklı,Hakan Ürey,Kaan Akşit
出处
期刊:Applied Optics
[Optica Publishing Group]
日期:2021-09-27
卷期号:61 (5): B50-B50
被引量:27
摘要
Computer-generated holography algorithms often fall short in matching simulations with results from a physical holographic display. Our work addresses this mismatch by learning the holographic light transport in holographic displays. Using a camera and a holographic display, we capture the image reconstructions of optimized holograms that rely on ideal simulations to generate a dataset. Inspired by the ideal simulations, we learn a complex-valued convolution kernel that can propagate given holograms to captured photographs in our dataset. Our method can dramatically improve simulation accuracy and image quality in holographic displays while paving the way for physically informed learning approaches.
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