52 citations · 109 across the 6 of their papers we have counts for
6 papers
Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)
Varun Mannam, Yide Zhang, Xiaotong Yuan +6
Fluorescence lifetime imaging microscopy (FLIM) systems are limited by their slow processing speed, low signal-to-noise ratio (SNR), and expensive and challenging hardware setups.…
Deep learning-based super-resolution fluorescence microscopy on small datasets
Varun Mannam, Yide Zhang, Xiaotong Yuan +1
Fluorescence microscopy has enabled a dramatic development in modern biology by visualizing biological organisms with micrometer scale resolution. However, due to the diffraction l…
Machine learning for faster and smarter fluorescence lifetime imaging microscopy
Varun Mannam, Yide Zhang, Xiaotong Yuan +2
Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique in biomedical research that uses the fluorophore decay rate to provide additional contrast in fluorescence m…
Instant Image Denoising Plugin for ImageJ using Convolutional Neural Networks
Varun Mannam, Yide Zhang, Yinhao Zhu +1
We present a new convolutional neural network (CNN) based ImageJ plugin for fluorescence microscopy image denoising with an average improvement of 7.5 dB in peak signal-to-noise ra…
A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images
Yide Zhang, Yinhao Zhu, Evan Nichols +4
Fluorescence microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, fluorescence microscopy is not only much noisier than photography,…
Super-resolution fluorescence microscopy by stepwise optical saturation
Yide Zhang, Prakash D. Nallathamby, Genevieve D. Vigil +5
Super-resolution fluorescence microscopy is an important tool in biomedical research for its ability to discern features smaller than the diffraction limit. However, due to its dif…