Recently, deep learning has yielded transformative success across optics and photonics, especially in optical metrology. Deep neural networks (DNNs) with a fully convolutional architecture (e.g., U-Net and its derivatives) have been widely implemented in an end-to-end manner to accomplish various optical metrology tasks, such as fringe denoising, phase unwrapping, and fringe analysis. However, the task of training a DNN to accurately identify an image-to-image transform from massive input and output data pairs seems at best na?ve, as the physical laws governing the image formation or other domain expertise pertaining to the measurement have not yet been fully exploited in current deep learning practice. To this end, we introduce a physics-informed deep learning method for fringe pattern analysis (PI-FPA) to overcome this limit by integrating a lightweight DNN with a learning-enhanced Fourier transform profilometry (LeFTP) module. By parameterizing conventional phase retrieval methods, the LeFTP module embeds the prior knowledge in the network structure and the loss function to directly provide reliable phase results for new types of samples, while circumventing the requirement of collecting a large amount of high-quality data in supervised learning methods. Guided by the initial phase from LeFTP, the phase recovery ability of the lightweight DNN is enhanced to further improve the phase accuracy at a low computational cost compared with existing end-to-end networks. Experimental results demonstrate that PI-FPA enables more accurate and computationally efficient single-shot phase retrieval, exhibiting its excellent generalization to various unseen objects during training. The proposed PI-FPA presents that challenging issues in optical metrology can be potentially overcome through the synergy of physics-priors-based traditional tools and data-driven learning approaches, opening new avenues to achieve fast and accurate single-shot 3D imaging.

We report tensorial tomographic Fourier ptychography (T²oFu), a nonscanning label-free tomographic microscopy method for simultaneous imaging of quantitative phase and anisotropic specimen information in 3D. Built upon Fourier ptychography, a quantitative phase imaging technique, T²oFu additionally highlights the vectorial nature of light. The imaging setup consists of a standard microscope equipped with an LED matrix, a polarization generator, and a polarization-sensitive camera. Permittivity tensors of anisotropic samples are computationally recovered from polarized intensity measurements across three dimensions. We demonstrate T²oFu’s efficiency through volumetric reconstructions of refractive index, birefringence, and orientation for various validation samples, as well as tissue samples from muscle fibers and diseased heart tissue. Our reconstructions of healthy muscle fibers reveal their 3D fine-filament structures with consistent orientations. Additionally, we demonstrate reconstructions of a heart tissue sample that carries important polarization information for detecting cardiac amyloidosis.

Lens-free on-chip microscopy with RGB LEDs (LFOCM-RGB) provides a portable, cost-effective, and high-throughput imaging tool for resource-limited environments. However, the weak coherence of LEDs limits the high-resolution imaging, and the luminous surfaces of the LED chips on the RGB LED do not overlap, making the coherence-enhanced executions tend to undermine the portable and cost-effective implementation. Here, we propose a specially designed pinhole array to enhance coherence in a portable and cost-effective implementation. It modulates the three-color beams from the RGB LED separately so that the three-color beams effectively overlap on the sample plane while reducing the effective light-emitting area for better spatial coherence. The separate modulation of the spatial coherence allows the temporal coherence to be modulated separately by single spectral filters rather than by expensive triple spectral filters. Based on the pinhole array, the LFOCM-RGB simply and effectively realizes the high-resolution imaging in a portable and cost-effective implementation, offering much flexibility for various applications in resource-limited environments.

利用基于光学记忆效应的单帧散斑自相关方法，研究了光透过随机散射介质的快速成像。短的相机曝光时间内的高质量快速成像需要尽可能消除影响成像质量的因素。通过引入旋转散射片来消除光束的空间相干性，避免相干噪声对成像质量的影响。光斑对比度可衡量光束的空间相干性被消除的效果，影响光斑对比度数值的主要因素有三个：旋转散射片介质颗粒度即目数、转速、相机曝光时间。实验分析了220目数和600目数两种旋转散射片和不同转速、相机不同曝光时间的情况。结果表明，转速提高和相机曝光时间的增加均使得光斑对比度下降并提升散斑相关成像质量，相机曝光时间超过一定值后，光斑对比度和成像相关系数随散射片转速和曝光时间的变化相对较小。因此对于相机曝光时间短的单帧散斑快速成像，选择最合适的散射片转速对高质量成像非常重要。通过优化算法来提升成像质量。根据对光学传递函数约束的迭代算法，无需利用目标的先验信息即可恢复系统的点扩展函数，该点扩展函数适用于不同形状、不同大小的目标，结合单帧散斑自相关算法可实现快速成像，与仅使用单帧散斑自相关算法的情况相比成像质量显著提升。

The spatial distribution of beams with orbital angular momentum in the far field is known to be extremely sensitive to angular aberrations, such as astigmatism, coma and trefoil. This poses a challenge for conventional beam optimization strategies when a homogeneous ring intensity is required for an application. We developed a novel approach for estimating the Zernike coefficients of low-order angular aberrations in the near field based solely on the analysis of the ring deformations in the far field. A fast, iterative reconstruction of the focal ring recreates the deformations and provides insight into the wavefront deformations in the near field without relying on conventional phase retrieval approaches. The output of our algorithm can be used to optimize the focal ring, as demonstrated experimentally at the 100 TW beamline at the Extreme Light Infrastructure - Nuclear Physics facility.