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Deep-Learning-Based Virtual Refocusing of Images Using an Engineered Point-Spread Function
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DOI：10.1021/acsphotonics.1c00660.png)
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Abstract
En 中文
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 similar to 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.
Keywords:
deep learning
super-resolution
extended depth of field
point-spread-function engineering
cascaded neural networks
computational microscopy
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