Extended depth-of-field in superresolved image reconstruction on a compound-eye camera

Author(s):  
Tomoya Nakamura ◽  
Ryoichi Horisaki ◽  
Jun Tanida
2012 ◽  
Vol 20 (25) ◽  
pp. 27482 ◽  
Author(s):  
Tomoya Nakamura ◽  
Ryoichi Horisaki ◽  
Jun Tanida

2012 ◽  
Vol 95 (11) ◽  
pp. 14-27 ◽  
Author(s):  
Keiichiro Kagawa ◽  
Kenji Yamada ◽  
Eiji Tanaka ◽  
Jun Tanida

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Luzhe Huang ◽  
Hanlong Chen ◽  
Yilin Luo ◽  
Yair Rivenson ◽  
Aydogan Ozcan

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.


2010 ◽  
Vol 17 (2) ◽  
pp. 515-520 ◽  
Author(s):  
Luis Rogerio de Oliveira Hein ◽  
Kamila Amato de Campos ◽  
Pietro Carelli Reis de Oliveira Caltabiano ◽  
Ana Lúcia Horovistiz

2009 ◽  
Vol 34 (11) ◽  
pp. 1684 ◽  
Author(s):  
Nicolas Olivier ◽  
Alexandre Mermillod-Blondin ◽  
Craig B. Arnold ◽  
Emmanuel Beaurepaire

2016 ◽  
Author(s):  
Andrea Curatolo ◽  
Martin Villiger ◽  
Dirk Lorenser ◽  
Philip Wijesinghe ◽  
Alexander Fritz ◽  
...  

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