Pollen can lead to individuals suffering from allergic rhinitis, with a person’s vulnerability being dependent on the species and the amount of pollen. Therefore, the ability to precisely quantify both the number and species of pollen grains in a certain volume would be invaluable. Lensless sensing offers the ability to classify pollen grains from their scattering patterns, with the use of very few optical components. However, since there could be 1000 s of species of pollen one may wish to identify, in order to avoid having to collect scattering patterns from all species (and mixtures of species) we propose using two separate neural networks. The first neural network generates a microscope equivalent image from the scattering pattern, having been trained on a limited number of experimentally collected pollen scattering data. The second neural network segments the generated image into its components, having been trained on microscope images, allowing pollen species identification (potentially allowing the use of existing databases of microscope images to expand range of species identified by the segmentation network). In addition to classification, segmentation also provides richer information, such as the number of pixels and therefore the potential size of particular pollen grains. Specifically, we demonstrate the identification and projected area of pollen grain species, via semantic image segmentation, in generated microscope images of pollen grains, containing mixtures and species that were previously unseen by the image generation network. The microscope images of mixtures of pollen grains, used for training the segmentation neural network, were created by fusing microscope images of isolated pollen grains together while the trained neural network was tested on microscope images of actual mixtures. The ability to carry out pollen species identification from reconstructed images without needing to train the identification network on the scattering patterns is useful for the real-world implementation of such technology.

花粉会导致个体患上过敏性鼻炎，而一个人的脆弱性取决于花粉的种类和数量。因此，精确量化一定体积内花粉粒的数量和种类的能力将是无价的。无透镜传感提供了从花粉粒的散射模式中对花粉粒进行分类的能力，只需使用很少的光学元件。然而，由于可能有 1000 s 的花粉种类，人们可能希望识别，为了避免必须收集所有物种（和物种混合物）的散射模式，我们建议使用两个独立的神经网络。第一个神经网络根据散射模式生成显微镜等效图像，并根据有限数量的实验收集的花粉散射数据进行训练。第二个神经网络将生成的图像分割成它的组成部分，在显微镜图像上进行训练，允许花粉物种识别（可能允许使用现有的显微镜图像数据库来扩大分割网络识别的物种范围）。除了分类之外，分割还提供更丰富的信息，例如像素数量以及特定花粉粒的潜在大小。具体来说，我们通过语义图像分割，在生成的花粉粒显微镜图像中展示了花粉粒物种的识别和投影面积，其中包含图像生成网络以前看不到的混合物和物种。花粉粒混合物的显微镜图像，用于训练分割神经网络，是通过将孤立花粉粒的显微镜图像融合在一起而创建的，同时在实际混合物的显微镜图像上测试训练后的神经网络。从重建图像中进行花粉种类识别而无需在散射模式上训练识别网络的能力对于此类技术的实际实施非常有用。