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Continuous Localization Using Sparsity Constraints for High-Density Super-Resolution Microscopy

J. Min, C. Vonesch, N. Olivier, H. Kirshner, S. Manley, J.C. Ye, M. Unser

Best student paper award, Proceedings of the Tenth IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'13), San Francisco CA, USA, April 7-11, 2013, pp. 181-184.

Super-resolution localization microscopy relies on sparse activation of photo-switchable probes. Such activation, however, introduces limited temporal resolution. High-density imaging overcomes this limitation by allowing several neighboring probes to be activated simultaneously. In this work, we propose an algorithm that incorporates a continuous-domain sparsity prior into the high-density localization problem. We use a Taylor approximation of the PSF, and rely on a fast proximal gradient optimization procedure. Unlike currently available methods that use discrete-domain sparsity priors, our approach does not restrict the estimated locations to a pre-defined sampling grid. Experimental results of simulated and real data demonstrate significant improvement over these methods in terms of accuracy, molecular identification and computational complexity.

AUTHOR="Min, J. and Vonesch, C. and Olivier, N. and Kirshner, H. and
        Manley, S. and Ye, J.C. and Unser, M.",
TITLE="Continuous Localization Using Sparsity Constraints for
        High-Density Super-Resolution Microscopy",
BOOKTITLE="Proceedings of the Tenth IEEE International Symposium on
        Biomedical Imaging: {F}rom Nano to Macro ({ISBI'13})",
address="San Francisco CA, USA",
month="April 7-11,",
note="Best student paper award")

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