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Dual-wavelength in-line digital holography with untrained deep neural networks
Photonics Research ( IF 7.6 ) Pub Date : 2021-12-01 , DOI: 10.1364/prj.441054
Chen Bai , Tong Peng , Junwei Min , Runze Li , Yuan Zhou , Baoli Yao

Dual-wavelength in-line digital holography (DIDH) is one of the popular methods for quantitative phase imaging of objects with non-contact and high-accuracy features. Two technical challenges in the reconstruction of these objects include suppressing the amplified noise and the twin-image that respectively originate from the phase difference and the phase-conjugated wavefronts. In contrast to the conventional methods, the deep learning network has become a powerful tool for estimating phase information in DIDH with the assistance of noise suppressing or twin-image removing ability. However, most of the current deep learning-based methods rely on supervised learning and training instances, thereby resulting in weakness when it comes to applying this training to practical imaging settings. In this paper, a new DIDH network (DIDH-Net) is proposed, which encapsulates the prior image information and the physical imaging process in an untrained deep neural network. The DIDH-Net can effectively suppress the amplified noise and the twin-image of the DIDH simultaneously by automatically adjusting the weights of the network. The obtained results demonstrate that the proposed method with robust phase reconstruction is well suited to improve the imaging performance of DIDH.

中文翻译:

具有未经训练的深度神经网络的双波长在线数字全息术

双波长在线数字全息 (DIDH) 是对具有非接触和高精度特征的物体进行定量相位成像的流行方法之一。重建这些物体的两个技术挑战包括抑制分别源自相位差和相位共轭波前的放大噪声和孪生图像。与传统方法相比,深度学习网络借助噪声抑制或双图像去除能力,已成为估计 DIDH 相位信息的有力工具。然而,目前大多数基于深度学习的方法依赖于监督学习和训练实例,因此在将这种训练应用于实际成像设置时存在弱点。在本文中,提出了一种新的 DIDH 网络(DIDH-Net),它将先验图像信息和物理成像过程封装在未经训练的深度神经网络中。DIDH-Net 通过自动调整网络的权重,可以同时有效抑制放大的噪声和 DIDH 的孪生图像。获得的结果表明,所提出的具有鲁棒相位重建的方法非常适合提高 DIDH 的成像性能。
更新日期:2021-12-01
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