We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively low-carrier frequency holograms—deep learning assisted variational Hilbert quantitative phase imaging (DL-VHQPI). The method, incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation, reliably and robustly recovers the quantitative phase information of the test objects. It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system. Compared to the conventional end-to-end networks (without a physical model), the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization. The DL-VHQPI is quantitatively studied by numerical simulation. The live-cell experiment is designed to demonstrate the method's practicality in biological research. The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.

Quantitative phase imaging (QPI) by differential phase contrast (DPC) with partially coherent illumination provides speckle-free imaging and lateral resolution beyond the coherent diffraction limit, demonstrating great potential in biomedical imaging applications. Generally, DPC employs weak object approximation to linearize the phase-to-intensity image formation, simplifying the solution to the phase retrieval as a two-dimensional deconvolution with the corresponding phase transfer function. Despite its widespread adoption, weak object approximation still lacks a precise and clear definition, suggesting that the accuracy of the QPI results, especially for samples with large phase values, is yet to be verified. In this paper, we analyze the weak object approximation condition quantitatively and explicitly give its strict definition that is applicable to arbitrary samples and illumination apertures. Furthermore, an iterative deconvolution QPI technique based on pseudo-weak object approximation is proposed to overcome the difficulty of applying DPC to large-phase samples without additional data acquisition. Experiments with standard microlens arrays and MCF-7 cells demonstrated that the proposed method can effectively extend DPC beyond weak object approximation to high-precision three-dimensional morphological characterization of large-phase technical and biological samples.

Transport of intensity equation (TIE) is a well-established non-interferometric phase retrieval approach that enables quantitative phase imaging (QPI) by simply measuring intensity images at multiple axially displaced planes. The advantage of a TIE-based QPI system is its compatibility with partially coherent illumination, which provides speckle-free imaging with resolution beyond the coherent diffraction limit. However, TIE is generally implemented with a brightfield (BF) configuration, and the maximum achievable imaging resolution is still limited to the incoherent diffraction limit (twice the coherent diffraction limit). It is desirable that TIE-related approaches can surpass this limit and achieve high-throughput [high-resolution and wide field of view (FOV)] QPI. We propose a hybrid BF and darkfield transport of intensity (HBDTI) approach for high-throughput quantitative phase microscopy. Two through-focus intensity stacks corresponding to BF and darkfield illuminations are acquired through a low-numerical-aperture (NA) objective lens. The high-resolution and large-FOV complex amplitude (both quantitative absorption and phase distributions) can then be synthesized based on an iterative phase retrieval algorithm taking the coherence model decomposition into account. The effectiveness of the proposed method is experimentally verified by the retrieval of the USAF resolution target and different types of biological cells. The experimental results demonstrate that the half-width imaging resolution can be improved from 1230 nm to 488 nm with 2.5 × expansion across a 4 × FOV of 7.19 mm², corresponding to a 6.25 × increase in space-bandwidth product from ∼5 to ∼30.2 megapixels. In contrast to conventional TIE-based QPI methods where only BF illumination is used, the synthetic aperture process of HBDTI further incorporates darkfield illuminations to expand the accessible object frequency, thereby significantly extending the maximum available resolution from 2NA to ∼5NA with a ∼5 × promotion of the coherent diffraction limit. Given its capability for high-throughput QPI, the proposed HBDTI approach is expected to be adopted in biomedical fields, such as personalized genomics and cancer diagnostics.

计算光学显微成像技术将光学编码和计算解码相结合，通过光学操作和图像算法重建来恢复微观物体的多维信息，为显微成像技术突破传统成像能力提供了强大的助力。这项技术的发展得益于现代光学系统、图像传感器以及高性能数据处理设备的优化，同时也被先进的通信技术和设备的发展所赋能。智能手机平台作为高度集成化的电子设备，具有先进的图像传感器和高性能的处理器，可以采集光学系统的图像并运行图像处理算法，为计算光学显微成像技术的实现创造了全新的方式。进一步地，作为可移动通信终端，智能手机平台开放的操作系统和多样的无线网络接入方法，赋予了显微镜灵活智能化操控能力与丰富的显示和处理分析功能，可用于实现各种复杂环境下多样化的生物学检测应用。文中从四个方面综述了基于智能手机平台的计算光学显微成像技术，首先综述了智能手机平台作为光学成像器件的新型显微成像光路设计，接下来介绍了基于智能手机平台先进传感器的计算光学高通量显微成像技术，然后介绍了智能手机平台的数据处理能力和互联能力在计算显微成像中的应用，最后讨论了这项技术现存在的一些问题及解决方向。