Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing. However, large-scale high-fidelity single-photon imaging remains a big challenge, due to the complex hardware manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging over an order of magnitude, with significant enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 $\times$ 32 pixels, 90 scenes, 10 different bit depth, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this real-world physical noise model, we for the first time synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depth, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique on a series of experiments including macroscopic and microscopic imaging, microfluidic inspection, and Fourier ptychography. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance, with more than 5 dB superiority on PSNR compared to the existing methods.

我们报告了张力层造影差异相位对比度显微镜（T2DPC），这是一种用于同时测量相和各向异性的无定量标签层析成像方法。T2DPC扩展了差异相位对比显微镜（一种定量相成像技术），以突出光的矢量性质。该方法求解了从配备有LED矩阵，圆极偏振器和偏振敏感摄像机的标准显微镜获得的强度测量的各向异性样品的介电常数张量。我们证明了各种验证样品的折射率，双折射和方向的准确体积重建，并证明生物标本的重建极化结构是病理学的预测。

在各种科学和临床环境中，快速无创探测空间变化的非相关事件（例如人类头骨下方的脑血流）是一项必不可少的任务。所使用的主要光学技术之一是弥漫性相关光谱（DC），其经典实现使用单个或几个单光子检测器，导致空间定位精度较差，时间分辨率相对较低。 Here, we propose a technique termed Classifying Rapid decorrelation Events via Parallelized single photon dEtection (CREPE)}, a new form of DCS that can probe and classify different decorrelating movements hidden underneath turbid volume with high sensitivity using parallelized speckle detection from a $32\times32 $像素SPAD阵列。我们通过对隐藏在5mm组织样的幻影下的不同时空 - 偏置模式进行分类来评估我们的设置，该模式由快速反相关的动态散射介质制成。十二个多模式纤维用于从组织幻影表面的不同位置收集散射光。为了验证我们的设置，我们通过在Multi-Kilo-Hertz速率下调制的数字微龙器设备（DMD）以及含有流动流体的容器幻影。除了具有胜过经典无监督学习方法的深层对比学习算法外，我们证明我们的方法可以准确地检测和分类浊度散射介质下的不同瞬态去相关事件（发生在0.1-0.4s中），而无需任何数据标记。这有可能应用于非侵入性的深层组织运动模式，例如在紧凑和静态检测探针内以多赫兹速率识别正常或异常的脑血流事件。

通过动态散射介质进行非侵入性光学成像具有许多重要的生物医学应用，但仍然是一项艰巨的任务。尽管标准弥漫成像方法测量光吸收或荧光发射，但也良好的是，散射的相干光的时间相关性通过组织像光强度一样扩散。然而，迄今为止，很少有作品旨在通过实验测量和处理这种时间相关数据，以证明去相关动力学的深度组织视频重建。在这项工作中，我们利用单光子雪崩二极管（SPAD）阵列摄像机同时监视单photon水平的斑点波动的时间动力学，从12种不同的幻影组织通过定制的纤维束阵列传递的位置。然后，我们应用深度神经网络将所获得的单光子测量值转换为迅速去摩擦组织幻像下散射动力学的视频。我们证明了重建瞬态（0.1-0.4s）动态事件的图像的能力，该动态事件发生在非相关的组织幻影下，并以毫米级分辨率进行重构，并突出显示我们的模型如何灵活地扩展到埋藏的phantom船只内的流速。