A method of spectrum estimation based on the genetic simulated annealing (GSA) algorithm is proposed, which is applied to retrieve the three-dimensional wind field of typhoon Nangka observed by our research group. Compared to the genetic algorithm (GA), the GSA algorithm not only extends the detection range and guarantees the accuracy of retrieval results but also demonstrates a faster retrieval speed. Experimental results indicate that both the GA and GSA algorithms can enhance the detection range by 35% more than the least squares method. However, the convergence speed of the GSA algorithm is 17 times faster than that of the GA, which is more beneficial for real-time data processing.

利用卫星遥感反演水体中的悬浮物浓度对水质监测和保护具有重要意义，在悬浮物浓度反演过程中，如何避免或最大程度降低水体中叶绿素a、有色可溶性有机物（Colored Dissolved Organic Matter，CDOM）的干扰是当前的技术难点。文章针对可持续发展科学卫星1号（SDGSAT-1）MII传感器，利用Hydrolight辐射传输模型，从理论上挖掘只与悬浮物强相关的反演因子，以此构建适用于MII影像的太湖悬浮物浓度反演模型，通过水体的实测数据和遥感数据对模型应用效果进行验证。结果表明：反演因子$ R'{\text{(}}{B_{\text{5}}}{\text{/}}{B_{\text{3}}}{\text{)}} $与悬浮物浓度为强相关，同时与叶绿素a、CDOM浓度弱相关；利用$ R'{\text{(}}{B_{\text{5}}}{\text{/}}{B_{\text{3}}}{\text{)}} $作为反演因子构建的幂函数模型为最优反演模型；将幂函数模型分别应用于实测数据和2022年5月4日的太湖SDGSAT-1 MII数据，两次验证试验显示反演结果和现场测量结果具有较强一致性，模型适用性较好。该研究可为SDGSAT-1卫星在湖泊水体悬浮物浓度监测、水资源评估与保护等提供一些技术参考。

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.