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Nonlinear reconstruction from binary measurements

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Information theory is a fast-growing area in mathematics, which also changes our everyday life. Compression and efficient reconstruction allow us to acquire and store a large amount of music, text and images on our electric devices every day.

In this talk I will discuss the special case of binary measurements, which occur in fluorescence microscopy, single pixel cameras and other analogue measurement devices with an “on-off” behaviour. We will look at linear and nonlinear reconstruction techniques and discuss the number of necessary samples and their location for an accurate and stable reconstruction. In the end I will talk briefly about current work on extending this work to higher dimensions and how it compares to neural network reconstruction.

This talk is part of the Cambridge Analysts' Knowledge Exchange series.

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