48 citations · 48 across the 1 of their papers we have counts for
5 papers
Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks
Luzhe Huang, Tairan Liu, Xilin Yang +3
Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging. Recovery of the missing phase information of a hologram is an important st…
Deep learning-based virtual refocusing of images using an engineered point-spread function
Xilin Yang, Luzhe Huang, Yilin Luo +4
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 netwo…
Recurrent neural network-based volumetric fluorescence microscopy
Luzhe Huang, Yilin Luo, Yair Rivenson +1
Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-…
Single-shot autofocusing of microscopy images using deep learning
Yilin Luo, Luzhe Huang, Yair Rivenson +1
We demonstrate a deep learning-based offline autofocusing method, termed Deep-R, that is trained to rapidly and blindly autofocus a single-shot microscopy image of a specimen that…
SNR-adaptive OCT angiography enabled by statistical characterization of intensity and decorrelation with multi-variate time series model
Luzhe Huang, Yiming Fu, Ruixiang Chen +6
In OCT angiography (OCTA), decorrelation computation has been widely used as a local motion index to identify dynamic flow from static tissues, but its dependence on SNR severely d…