Single-shot speckle correlation fluorescence microscopy in thick scattering tissue with image reconstruction priors

2018 ◽  
Vol 11 (3) ◽  
pp. e201700224 ◽  
Author(s):  
Julie Chang ◽  
Gordon Wetzstein
Keyword(s):  
Image Reconstruction ◽  
Fluorescence Microscopy ◽  
Single Shot ◽  
Speckle Correlation

scholarly journals Recurrent neural network-based volumetric fluorescence microscopy

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Luzhe Huang ◽  
Hanlong Chen ◽  
Yilin Luo ◽  
Yair Rivenson ◽  
Aydogan Ozcan
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Fluorescence Microscopy ◽  
Recurrent Network ◽  
Sample Volume ◽  
Depth Of Field ◽  
Objective Lens ◽  
Wide Field ◽  
Volumetric Imaging ◽  
3D Image Reconstruction

AbstractVolumetric 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-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample volume. Through a recurrent convolutional neural network, which we term as Recurrent-MZ, 2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended depth-of-field. Using experiments on C. elegans and nanobead samples, Recurrent-MZ is demonstrated to significantly increase the depth-of-field of a 63×/1.4NA objective lens, also providing a 30-fold reduction in the number of axial scans required to image the same sample volume. We further illustrated the generalization of this recurrent network for 3D imaging by showing its resilience to varying imaging conditions, including e.g., different sequences of input images, covering various axial permutations and unknown axial positioning errors. We also demonstrated wide-field to confocal cross-modality image transformations using Recurrent-MZ framework and performed 3D image reconstruction of a sample using a few wide-field 2D fluorescence images as input, matching confocal microscopy images of the same sample volume. Recurrent-MZ demonstrates the first application of recurrent neural networks in microscopic image reconstruction and provides a flexible and rapid volumetric imaging framework, overcoming the limitations of current 3D scanning microscopy tools.

Multispectral Bayesian reconstruction technique for real-time two color fluorescence microscopy

RSC Advances ◽  
2015 ◽  
Vol 5 (17) ◽  
pp. 13175-13183 ◽  
Author(s):  
Shilpa Dilipkumar ◽  
Ravi Manjithaya ◽  
Partha Pratim Mondal
Keyword(s):  
Image Reconstruction ◽  
Fluorescence Microscopy ◽  
Real Time ◽  
Spectral Imaging ◽  
Reconstruction Technique ◽  
Imaging Method ◽  
Combined Approach ◽  
Bayesian Reconstruction ◽  
Image Reconstruction Technique ◽  
Bayesian Image Reconstruction

We have developed a real-time imaging method for two-color widefield fluorescence microscopy using a combined approach that integrates multi-spectral imaging and Bayesian image reconstruction technique.

A marginal image reconstruction approach in fluorescence microscopy with pseudo-random illumination patterns

2016 ◽  
Author(s):  
Penghuan Liu ◽  
Sébastien Bourguignon ◽  
Jérome Idier ◽  
Marc Allain ◽  
Simon Labouesse ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Fluorescence Microscopy ◽  
Random Illumination

scholarly journals Fast image reconstruction for fluorescence microscopy

AIP Advances ◽  
2012 ◽  
Vol 2 (3) ◽  
pp. 032174 ◽  
Author(s):  
Mahesh Ravi Varma ◽  
K. Rajan ◽  
Partha Pratim Mondal
Keyword(s):  
Image Reconstruction ◽  
Fluorescence Microscopy

scholarly journals Low power super resolution fluorescence microscopy by lifetime modification and image reconstruction

2014 ◽  
Vol 22 (10) ◽  
pp. 12327 ◽  
Author(s):  
Richard J. Marsh ◽  
Siân Culley ◽  
Angus J. Bain
Keyword(s):  
Image Reconstruction ◽  
Fluorescence Microscopy ◽  
Low Power ◽  
Super Resolution
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