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.

不同于普通图像压缩，多光谱图像压缩除了需要去除空间冗余同时还需要去除光谱间冗余，近年来研究表明端到端的卷积神经网络模型在图像压缩方面具有很好的性能，但对于多光谱图像压缩其编解码器并不能有效解决同时高效提取到多光谱图像空间和光谱间特征的问题，同时也会忽略图像局部特征信息。针对以上问题，本文提出了一种融合多尺度特征卷积神经网络的多光谱图像压缩方法。所提出网络在压缩模型的编解码器中嵌入了可以提取出不同尺度下空间和光谱间特征信息的多尺度特征提取模块，以及可以用来捕捉局部空间信息和光谱信息的空间光谱间非对称卷积模块。实验表明，与传统算法如JPEG2000和3D-SPIHT以及深度学习方法相比，在Landsat-8的7波段和Sentinel-2的8波段数据集上所提出模型的峰值信噪比（PSNR）指标高于传统算法1-2dB。在平均光谱角度（MSA）指标的衡量下，所提出的模型在Landsat-8数据集上优于传统算法约8×10^-3 rad，在Sentinel-2数据集上优于传统算法约2×10^-3 rad。满足了多光谱图像压缩对空间和光谱间特征提取以及局部特征提取的要求。

为满足点衍射干涉测量对解包算法高精度、高效以及抗干扰的检测需求。提出一种基于空洞空间卷积的相移点衍射干涉图像的相位解包方法，通过将自编码器结构和空洞空间卷积结合获得更高的相位解包精度，实现对包裹相位图像可控的多尺度特征提取。根据点衍射图像特点制作的大量多样化数据集对其进行训练和优化，从而实现准确识别包裹相位所在阶次，最终可以快速处理包裹图像得到高精度的解包结果。利用所提方法对实际点衍射干涉图像进行处理，并与ESDI专业干涉图像处理软件以及其他解包算法处理结果进行比对，结果表明：本文解包结果与软件枝切法解包处理结果均方根误差值为0.022 2 rad，面形拟合结果与软件面形拟合结果峰谷差值仅为0.012 1λ、均方根差值仅为0.004 2λ；时间效率上，完成一幅图像的处理平均仅需0.035 s，而传统方法均大于1 s。与其他方法相比，所提方法在处理包裹相位方面具有快速、高精度的特性，为点衍射干涉图像处理的高精度相位解包提供了新的可行方案。

The ever-increasing demand for training and inferring with larger machine-learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power computation, since light propagation through a nonabsorbing medium is a lossless operation. However, to carry out useful and efficient computations with light, generating and controlling nonlinearity optically is a necessity that is still elusive. Multimode fibers (MMFs) have been shown that they can provide nonlinear effects with microwatts of average power while maintaining parallelism and low loss. We propose an optical neural network architecture that performs nonlinear optical computation by controlling the propagation of ultrashort pulses in MMF by wavefront shaping. With a surrogate model, optimal sets of parameters are found to program this optical computer for different tasks with minimal utilization of an electronic computer. We show a remarkable decrease of 97% in the number of model parameters, which leads to an overall 99% digital operation reduction compared to an equivalently performing digital neural network. We further demonstrate that a fully optical implementation can also be performed with competitive accuracies.

As an optical processor, a diffractive deep neural network (D²NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D²NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D²NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D²NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.