Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-image-free phase retrieval from single-shot hologram

扫码查看

原文链接

万方数据

外文摘要：Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of liv-ing cells'morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are data-driven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Here-in,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.

外文关键词：

optical microscopyquantitative phase imagingdigital holographic microscopydeep learningsuper-resolution

作者：

Xuan Tian、Runze Li、Tong Peng、Yuge Xue、Junwei Min、Xing Li、Chen Bai、Baoli Yao

展开 >

作者单位：

State Key Laboratory of Transient Optics and Photonics,Xi'an Institute of Optics and Precision Mechanics,Chinese Academy of Sciences,Xi'an 710119,China

University of Chinese Academy of Sciences,Beijing 100049,China

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaNational Key Research and Development Program of ChinaYouth Innovation Promotion Association,CAS

项目编号：

622752676233501812127805621053592021YFF07003032022YFE01007002021401

出版年：

2024

DOI：

10.29026/oea.2024.240060

光电进展(英文版)

ISSN：

年,卷(期)：2024.7(9)