基于模型训练卷积神经网络的数字Gabor全息图绘制实验
Experimental digital Gabor hologram rendering by a model-trained convolutional neural network
摘要：
数字全息图渲染可以通过卷积神经网络来执行，该卷积神经网络用通过来自稀疏生成图像的数值波传播计算的图像对来训练。512 × 512像素的数字伽柏幅度全息图成功地估计从实验干涉图的标准UNet训练与50,000合成图像对超过70个历元。
英文摘要：
Digital hologram rendering can be performed by a convolutional neural network, trained with image pairs calculated by numerical wave propagation from sparse generating images. 512-by-512 pixeldigital Gabor magnitude holograms are successfully estimated from experimental interferograms by a standard UNet trained with 50,000 synthetic image pairs over 70 epochs.
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。
