学习全息光传输
Learned holographic light transport
摘要：
计算机生成全息（CGH）算法通常无法将模拟结果与物理全息显示的结果相匹配。我们的工作通过学习全息显示器中的全息光传输来解决这种不匹配。使用相机和全息显示器，我们捕获依赖于理想模拟来生成数据集的优化全息图的图像重建。受理想模拟的启发，我们学习了一个复值卷积核，它可以将给定的全息图传播到我们数据集中捕获的照片。我们的方法可以显着提高全息显示器的模拟精度和图像质量，同时为物理学习方法铺平道路。
英文摘要：
Computer-Generated Holography (CGH) 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
