Deep learning-based virtual refocusing of images using an engineered point-spread function

• URL: http://arxiv.org/abs/2012.11892v1
• Date: Tue, 22 Dec 2020 09:15:26 GMT
• Title: Deep learning-based virtual refocusing of images using an engineered point-spread function
• Authors: Xilin Yang, Luzhe Huang, Yilin Luo, Yichen Wu, Hongda Wang, Yair Rivenson, and Aydogan Ozcan
• Abstract summary: 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) We extend the DOF of a fluorescence microscope by 20-fold.
• Score: 1.2977570993112095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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