显微镜
计算机科学
人工智能
显微镜
景深
深度学习
图像分辨率
分辨率(逻辑)
计算机视觉
采样(信号处理)
光圈(计算机存储器)
光学
物理
声学
滤波器(信号处理)
作者
Lingbo Jin,Yubo Tang,Yi‐Cheng Wu,Jackson B. Coole,Melody T. Tan,Xuan Zhao,Hawraa Badaoui,Jacob T. Robinson,Michelle D. Williams,Ann M. Gillenwater,Rebecca Richards‐Kortum,Ashok Veeraraghavan
标识
DOI:10.1073/pnas.2013571117
摘要
Significance Traditional microscopy suffers from a fixed trade-off between depth-of-field (DOF) and spatial resolution—the higher the desired spatial resolution, the narrower the DOF. We present DeepDOF, a computational microscope that allows us to break free from this constraint and achieve >5× larger DOF while retaining cellular-resolution imaging—obviating the need for z-scanning and significantly reducing the time needed for imaging. The key ingredients that allow this advance are 1) an optimized phase mask placed at the microscope aperture; and 2) a deep-learning-based algorithm that turns sensor data into high-resolution, large-DOF images. DeepDOF offers an inexpensive means for fast and slide-free histology, suited for improving tissue sampling during intraoperative assessment and in resource-constrained settings.
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