Biomedical Imaging GroupSTI
English only   BIG > Publications > Superresolution Microscopy

 Home Page
 News & Events
 Tutorials and Reviews
 Download Algorithms

 All BibTeX References

Fast Maximum Likelihood High-Density Low-SNR Super-Resolution Localization Microscopy

K. Kim, J. Min, L. Carlini, M. Unser, S. Manley, D. Jeon, J.C. Ye

Proceedings of the Tenth International Workshop on Sampling Theory and Applications (SampTA'13), Bremen, Federal Republic of Germany, July 1-5, 2013, pp. 285-288.

Localization microscopy such as STORM/PALM achieves the super-resolution by sparsely activating photo-switchable probes. However, to make the activation sparse enough to obtain reconstruction images using conventional algorithms, only small set of probes need to be activated simultaneously, which limits the temporal resolution. Hence, to improve temporal resolution up to a level of live cell imaging, high-density imaging algorithms that can resolve several overlapping PSFs are required. In this paper, we propose a maximum likelihood algorithm under Poisson noise model for the high-density low-SNR STORM/PALM imaging. Using a sparsity promoting prior with concave-convex procedure (CCCP) optimization algorithm, we achieved high performance reconstructions with fast reconstruction speed of 5 second per frame under high density low SNR imaging conditions. Experimental results using simulated and real live-cell imaging data demonstrate that proposed algorithm is more robust than previous methods in terms of both localization accuracy and molecular recall rate.

AUTHOR="Kim, K. and Min, J. and Carlini, L. and Unser, M. and Manley, S.
        and Jeon, D. and Ye, J.C.",
TITLE="Fast Maximum Likelihood High-Density Low-{SNR} Super-Resolution
        Localization Microscopy",
BOOKTITLE="Proceedings of the Tenth International Workshop on Sampling
        Theory and Applications ({SampTA'13})",
address="Bremen, Federal Republic of Germany",
month="July 1-5,",

© 2013 SampTA. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from SampTA.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.