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Found 5 papers, 5 papers with code
Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)
By integrating image denoising using the trained deep learning model on the FLIM data, provide accurate FLIM phasor measurements are obtained.
Deep learning-based super-resolution fluorescence microscopy on small datasets
However, using the new approach, a network can be trained to achieve super-resolution images from this small dataset.
Machine learning for faster and smarter fluorescence lifetime imaging microscopy
Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique in biomedical research that uses the fluorophore decay rate to provide additional contrast in fluorescence microscopy.
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Image Denoising
on FMD
A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images
In this paper, we fill this gap by constructing a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising.
Super-resolution fluorescence microscopy by stepwise optical saturation
For example, linearly combining (scaling and subtracting) two images obtained at regular powers extends resolution by a factor of $1. 4$ beyond the diffraction limit.
Optics Biological Physics
