Tag Archives: Neural Networks

Weekly recap (29/04/2018)

This week we have a lot of interesting stuff: Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms Adaptive Optics + Light Sheet Microscopy to see living cells inside the body of a Zebra fish (the favorite fish of biologists!). Really impressive images overcoming scattering caused by tissue. You can read more about the paper on

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The week in papers (22/04/18)

As a way to keep posts going, I am starting a short recap about interesting papers being published (or being discovered) every now and then. Probably I will write longer posts about some of them in the future. Let’s get this thing going: Two papers using ‘centroid estimation‘ to retrieve interesting information: Extract voice information using high-speed camera Mariko Akutsu, Yasuhiro

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Deep learning microscopy

This week a new paper by the group leaded by A. Ozcan appeared in Optica. Deep learning microscopy, Y. Ribenson et al, at Optica (featured image exctracted from Fig. 6 of the supplement) Abstract, We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field of view and depth of field. After

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Imaging through glass diffusers using densely connected convolutional networks

I just found a new paper by the group of G. Barbastathis at MIT. Imaging through glass diffusers using densely connected convolutional networks, S. Li et al, Submitted on 18 Nov 2017, https://arxiv.org/abs/1711.06810 (featured image from Fig. 3 of the manuscript) Abstract, Computational imaging through scatter generally is accomplished by first characterizing the scattering medium so that its forward operator is obtained;

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