Traditional fluorescence microscopy is constrained by inherent trade-offs among resolution, field of view, and system complexity. To navigate these challenges, we introduce a simple and low-cost computational multi-aperture miniature microscope, utilizing a microlens array for single-shot wide-field, high-resolution imaging. Addressing the challenges posed by extensive view multiplexing and non-local, shift-variant aberrations in this device, we present SV-FourierNet, a multi-channel Fourier neural network. SV-FourierNet facilitates high-resolution image reconstruction across the entire imaging field through its learned global receptive field. We establish a close relationship between the physical spatially varying point-spread functions and the network’s learned effective receptive field. This ensures that SV-FourierNet has effectively encapsulated the spatially varying aberrations in our system and learned a physically meaningful function for image reconstruction. Training of SV-FourierNet is conducted entirely on a physics-based simulator. We showcase wide-field, high-resolution video reconstructions on colonies of freely moving C. elegans and imaging of a mouse brain section. Our computational multi-aperture miniature microscope, augmented with SV-FourierNet, represents a major advancement in computational microscopy and may find broad applications in biomedical research and other fields requiring compact microscopy solutions.

中文翻译：

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使用傅里叶神经网络在计算多孔微型显微镜中进行宽视场、高分辨率重建


传统荧光显微镜受到分辨率、视场和系统复杂性之间固有权衡的限制。为了应对这些挑战，我们引入了一种简单且低成本的计算多孔微型显微镜，利用微透镜阵列进行单次宽视场高分辨率成像。为了解决该设备中广泛的视图复用和非局部、平移变异像差带来的挑战，我们提出了 SV-FourierNet，一种多通道傅里叶神经网络。 SV-FourierNet 通过其学习的全局感受野促进整个成像领域的高分辨率图像重建。我们在物理空间变化的点扩散函数和网络学习的有效感受野之间建立了密切的关系。这确保了 SV-FourierNet 有效地将空间变化的像差封装在我们的系统中，并学习了用于图像重建的物理上有意义的函数。 SV-FourierNet 的训练完全在基于物理的模拟器上进行。我们展示了自由移动菌落的宽视场、高分辨率视频重建线虫以及小鼠大脑切片的成像。我们的计算多孔微型显微镜通过 SV-FourierNet 进行增强，代表了计算显微镜的重大进步，可能会在生物医学研究和其他需要紧凑显微镜解决方案的领域找到广泛的应用。