论文标题
使用工程点扩展功能对图像进行深度基于学习的虚拟重新关注
Deep learning-based virtual refocusing of images using an engineered point-spread function
论文作者
论文摘要
我们在通过级联神经网络和双螺旋点传播函数（DH-PSF）启用的扩展景深（DOF）上提出了一个虚拟图像重新关注方法。该网络模型（称为W-NET）由两个级联的发生器和歧视网络对组成。第一个发电机网络学会了将输入图像实际重新集中在用户定义的平面上，而第二发电机则学会执行跨模式图像转换，从而改善了输出图像的横向分辨率。使用此W-NET模型与DH-PSF工程，我们将荧光显微镜的DOF延长了约20倍。该方法可用于开发具有深度学习的图像重建方法，用于定位显微镜技术，该方法利用工程的PSF来改善其成像性能，包括空间分辨率和体积成像吞吐量。
We present a virtual image 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 extend the DOF of a fluorescence microscope by ~20-fold. This approach can be applied to develop deep learning-enabled image reconstruction methods for localization microscopy techniques that utilize engineered PSFs to improve their imaging performance, including spatial resolution and volumetric imaging throughput.
