Niknam Farhad, Qazvini Hamed, Latifi Hamid
Laser and Plasma Research Institute, Shahid Beheshti University, Tehran, 1983963113, Iran.
Department of Physics, Shahid Beheshti University, Tehran, 1983963113, Iran.
Sci Rep. 2021 May 25;11(1):10903. doi: 10.1038/s41598-021-90312-5.
Image reconstruction using minimal measured information has been a long-standing open problem in many computational imaging approaches, in particular in-line holography. Many solutions are devised based on compressive sensing (CS) techniques with handcrafted image priors or supervised deep neural networks (DNN). However, the limited performance of CS methods due to lack of information about the image priors and the requirement of an enormous amount of per-sample-type training resources for DNNs has posed new challenges over the primary problem. In this study, we propose a single-shot lensless in-line holographic reconstruction method using an untrained deep neural network which is incorporated with a physical image formation algorithm. We demonstrate that by modifying a deep decoder network with simple regularizers, a Gabor hologram can be inversely reconstructed via a minimization process that is constrained by a deep image prior. The outcoming model allows to accurately recover the phase and amplitude images without any training dataset, excess measurements, or specific assumptions about the object's or the measurement's characteristics.
在许多计算成像方法中，尤其是在线全息术中，利用最少测量信息进行图像重建一直是一个长期存在的开放性问题。许多解决方案是基于具有手工制作图像先验的压缩感知（CS）技术或监督深度神经网络（DNN）设计的。然而，由于缺乏关于图像先验的信息，CS方法的性能有限，并且DNN需要大量的每种样本类型的训练资源，这给最初的问题带来了新的挑战。在本研究中，我们提出了一种使用未训练的深度神经网络并结合物理图像形成算法的单次无透镜在线全息重建方法。我们证明，通过用简单的正则化器修改深度解码器网络，可以通过受深度图像先验约束的最小化过程来反向重建Gabor全息图。由此产生的模型无需任何训练数据集、额外测量或关于物体或测量特征的特定假设，就能准确恢复相位和幅度图像。
