We present a virtual refocusing method over an extended depth of field (DOF) enabled by cascaded neural networks and a double-helix point-spread function (DH-PSF). This network model, referred to as W-Net, is composed of two cascaded generator and discriminator network pairs. The first generator network learns to virtually refocus an input image onto a user-defined plane, while the second generator learns to perform a cross-modality image transformation, improving the lateral resolution of the output image. Using this W-Net model with DH-PSF engineering, we experimentally extended the DOF of a fluorescence microscope by ∼20-fold. In addition to DH-PSF, we also report the application of this method to another spatially engineered imaging system that uses a tetrapod point-spread function. This approach can be widely used to develop deep-learning-enabled reconstruction methods for localization microscopy techniques that utilize engineered PSFs to considerably improve their imaging performance, including the spatial resolution and volumetric imaging throughput.

中文翻译：

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使用工程点扩散函数对图像进行基于深度学习的虚拟重新聚焦

我们在级联神经网络和双螺旋点扩散函数 (DH-PSF) 启用的扩展景深 (DOF) 上提出了一种虚拟重新聚焦方法。该网络模型称为 W-Net，由两个级联的生成器和鉴别器网络对组成。第一个生成器网络学习将输入图像虚拟地重新聚焦到用户定义的平面上，而第二个生成器学习执行跨模态图像变换，从而提高输出图像的横向分辨率。使用此 W-Net 模型和 DH-PSF 工程，我们通过实验将荧光显微镜的 DOF 扩展了约 20 倍。除了 DH-PSF，我们还报告了这种方法在另一个使用四足动物点扩散函数的空间工程成像系统中的应用。