Non-smooth convex optimization for an efficient reconstruction in structured illumination microscopy

This work aims at proposing a new reconstruction procedure for structured illumination microscopy. The proposed method is based on some recent development in non-smooth convex optimization that allows to deal with Poisson negative log-likelihood as data fidelity term and with regularization terms allowing to extract sharp features. The performances of the proposed method are compared to the state-of-the-art of SIM reconstruction techniques.

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