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Found 7 papers, 4 papers with code
Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method
Convolutional neural networks (CNNs) are a potential approach for object recognition and detection.
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.
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.
Ranked #1 on
Image Denoising
on FMD
Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs
Extracting large amounts of data from biological samples is not feasible due to radiation issues, and image processing in the small-data regime is one of the critical challenges when working with a limited amount of data.
