论文标题
通过模型训练的卷积神经网络渲染实验性数字Gabor全息图
Experimental digital Gabor hologram rendering by a model-trained convolutional neural network
论文作者
论文摘要
数字全息图渲染可以由卷积神经网络执行，该网络用图像对训练，该图像对通过稀疏生成图像的数值波传播计算出来。 512-BY-512 PIXELDIGITIAL GABOR幅度全息图成功地从实验干涉图中通过经过50,000个合成图像对训练的标准UNET在70个时代以上。
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.
