Tag Archives: Computational Imaging

Data fusion as a way to perform compressive sensing

Some time ago I started working on some kind of data fusion problem where we have access to several imaging systems working in parallel, each one gathering a different multidimensional dataset with mixed spectral, temporal, and/or spatial resolutions. The idea is to perform 4D imaging at high spectral, temporal, and spatial resolutions using some single-pixel/multi-pixel detectors, where each detector is

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Inverse Scattering via Transmission Matrices: Broadband Illumination and Fast Phase Retrieval Algorithms

Interesting paper by people at Rice and Northwestern universities about different phase retrieval algorithms for measuring transmission matrices without using interferometric techniques. The thing with interferometers is that they provide you lots of cool stuff (high sensibility, phase information, etc.), but also involve quite a lot of technical problems that you do not want to face every day in the

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Single-pixel imaging with sampling distributed over simplex vertices

Last week I posted a recently uploaded paper on using positive-only patterns in a single-pixel imaging system. Today I just found another implementation looking for the same objective. This time the authors (from University of Warsaw, leaded by Rafał Kotyński) introduce the idea of simplexes, or how any point in some N-dimensional space can be located using only positive coordinates

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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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