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Found 6 papers, 4 papers with code
Deconvolution in Fluorescence Lifetime imaging microscopy (FLIM)
Fluorescence lifetime imaging microscopy (FLIM) is an important technique to understand the chemical micro-environment in cells and tissues since it provides additional contrast compared to conventional fluorescence imaging.
Low dosage 3D volume fluorescence microscopy imaging using compressive sensing
To address this challenge, we present a compressive sensing (CS) based approach to fully reconstruct 3D volumes with the same signal-to-noise ratio (SNR) with less than half of the excitation dosage.
Spatial modeling of mid-infrared spectral data with thermal compensation using integrated nested Laplace approximation
The problem of analyzing substances using low-cost sensors with a low signal-to-noise ratio (SNR) remains challenging.
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.
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.
