全息术
计算机科学
深度学习
人工智能
全息显示器
图像质量
领域(数学)
镜头(地质)
光学
迭代重建
生成对抗网络
计算机视觉
图像(数学)
物理
数学
纯数学
作者
Tian Zhang,Ming Zhao,Aobing Qi,Fengqiang Li,Xinyi Yu,Yongxin Song
出处
期刊:Optics Letters
[The Optical Society]
日期:2022-08-16
卷期号:47 (17): 4283-4283
被引量:4
摘要
Lensless imaging has attracted attention as it avoids the bulky optical lens. Lensless holographic imaging is a type of a lensless imaging technique. Recently, deep learning has also shown tremendous potential in lensless holographic imaging. A labeled complex field including real and imaginary components of the samples is usually used as a training dataset. However, obtaining such a holographic dataset is challenging. In this Letter, we propose a lensless computational imaging technique with a hybrid framework of holographic propagation and deep learning. The proposed framework takes recorded holograms as input instead of complex fields, and compares the input and regenerated holograms. Compared to previous supervised learning schemes with a labeled complex field, our method does not require this supervision. Furthermore, we use the generative adversarial network to constrain the proposed framework and tackle the trivial solution. We demonstrate high-quality reconstruction with the proposed framework compared to previous deep learning methods.
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