A nonintrusive far‐field optical microscopy resolving structures at the nanometer scale would revolutionize biomedicine and nanotechnology but is not yet available. Here, a new type of microscopy is introduced, which reveals the fine structure of an object through its far‐field scattering pattern under illumination with light containing deeply subwavelength singularity features. The object is reconstructed by a neural network trained on a large number of scattering events. In numerical experiments on imaging of a dimer, resolving powers better than λ/200, i.e., two orders of magnitude beyond the conventional “diffraction limit” of λ/2, are demonstrated. It is shown that imaging is tolerant to noise and is achievable with low dynamic range light intensity detectors. Proof‐of‐principle experimental confirmation of DSTM is provided with a training set of small size, yet sufficient to achieve resolution five‐fold better than the diffraction limit. In principle, deep learning reconstruction can be extended to objects of random shape and shall be particularly efficient in microscopy of a priori known shapes, such as those found in routine tasks of machine vision, smart manufacturing, and particle counting for life sciences applications.

中文翻译：

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未标记的远场深亚波长拓扑显微镜（DSTM）

纳米级的非侵入式远场光学显微镜分辨结构将彻底改变生物医学和纳米技术，但目前尚无可用。在这里，介绍了一种新型的显微镜，它通过在具有深亚波长奇异性特征的光照射下，通过其远场散射模式揭示了物体的精细结构。通过在大量散射事件上训练的神经网络重建对象。在二聚体成像的数值实验中，分辨力优于λ / 200，即比传统的“衍射极限” λ高两个数量级/ 2，进行了演示。结果表明，成像是可以容忍噪声的，并且可以通过低动态范围的光强度检测器来实现。DSTM的原理验证实验确认提供了一个很小的训练集，但足以实现比衍射极限高五倍的分辨率。原则上，深度学习重建可以扩展到任意形状的对象，并且在显微镜下对先验已知形状的显微镜尤其有效，例如在机器视觉，智能制造和生命科学应用的粒子计数等常规任务中发现的形状。