单发
全息术
反褶积
弹丸
计算机科学
编码孔径
相关性
一次性
光圈(计算机存储器)
光学
维纳反褶积
人工智能
算法
物理
盲反褶积
材料科学
声学
数学
电信
机械工程
几何学
探测器
冶金
工程类
作者
Le Yang,Junpeng Yang,Huiyang Wang,Yuheng Wang,Tao Huang,Weina Zhang,Jianglei Di,Liyun Zhong
标识
DOI:10.1016/j.optlaseng.2024.108227
摘要
One of the challenges in interferenceless coded aperture correlation holography (I-COACH) is high-quality single-shot reconstruction, as conventional cross-correlation reconstruction algorithm requires recording multiple holograms and complex reconstruction operations, resulting in serious background noise. Here, we present a deep learning based single-shot reconstruction by introducing a learnable Wiener deconvolution network in I-COACH. By feeding the point spread hologram (PSH) of I-COACH system and object hologram (OH) into Wiener deconvolution, a mapping relationship between Wiener deconvolution result and ground truth is generated. A Res-UNet is trained to learn optimal filter and noise regularization parameters, thereby achieving high quality reconstruction. Moreover, the training dataset used in our network are simulated using PSH that is obtained through only one experiment acquisition in advance, greatly simplifying experimental OH dataset acquisition. Compared to the conventional correlation reconstruction methods, the proposed Wiener deconvolution network can conveniently realize high-quality single-shot reconstruction in I-COACH, and the axial sensitivity in 3D reconstruction can be demonstrated by varying scattering degree of coded phase mask. Importantly, this Wiener deconvolution solution will provide an enlightening reference for dynamic imaging in I-COACH.
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