The Taub Faculty of Computer Science Events and Talks

Fluorescence microscopy is a powerful tool in biology and biomedical sciences. Microscopic specimens usually yield fluorescence intensity images that are dim and thus suffer from low signal-to-noise ratio (SNR). Moreover, in multispectral imaging of fluorescing specimen, intensities are just a means to obtain information about molecular distributions of the materials in the specimen.

Multiplexed sensing is a way for increasing the signal-to-noise ratio (SNR) of intensity data arrays, without increasing acquisition resources such as time. However, as in fluorescence, these arrays themselves are often not the ultimate goal of a system. For example, spectral reflectance, emission or absorption distributions stem from an underlying mixture of materials. Systems thus try to infer concentrations of these underlying mixed components.

The process of inverting mixtures is termed unmixing. It is central in many problems. We incorporate the mixing/unmixing process together with detector noise characteristics explicitly into the optimization of multiplexing codes. This enables optimal recovery of underlying components (materials). In the absence of this integrated optimization, multiplexed imaging can even harm the quality of unmixing. Moreover, by directly defining the goal of data acquisition to be recovery of components (materials) instead of intensity/reflectance arrays, the acquisition becomes more efficient. We thus develop a theory for multiplexed sensing, in which the end task is linear unmixing. This yields significant generalizations of multiplexing theory.

* M.Sc. research under supervision of Prof. Yoav Shechner