Within the family of super-resolution (SR) fluorescence microscopy, single-molecule localization microscopies (PALM, STORM and their derivatives) afford among the highest spatial resolution (approximately 5 to 10 nm), but often with moderate temporal resolution. The high spatial resolution relies on the adequate accumulation of precise localizations, which requires a relatively low density of bright fluorophores. Several methods have demonstrated localization at higher densities in both two dimensions (2D)[3, 4] and three dimensions (3D)[5-7]. Additionally, with further advancements, such as functional super-resolution[8, 9] and point spread function (PSF) engineering with[8-11] or without multi-channel observations, extra information (spectra, dipole orientation) can be encoded and recovered at the single molecule level. However, such advancements are not fully extended for high-density conditions in 3D. In this work, we adopt sparse recovery using simple matrix/vector operations, and propose a systematic progressive refinement method (dubbed as PRIS) for 3D high-density condition. We also generalized the method for PSF engineering, multichannel and multi-species observations using different forms of matrix concatenations. Specifically, we demonstrate reconstructions with both double-helix and astigmatic PSFs, for both single and biplane settings. We also demonstrate the recovery capability for a mixture of two different color species.
|Title of host publication||Single Molecule Spectroscopy and Superresolution Imaging XII|
|Editors||Felix Koberling, Ingo Gregor, Zygmunt K. Gryczynski|
|State||Published - 2019|
|Event||Single Molecule Spectroscopy and Superresolution Imaging XII 2019 - San Francisco, United States|
Duration: 2 Feb 2019 → 3 Feb 2019
|Name||Progress in Biomedical Optics and Imaging - Proceedings of SPIE|
|Conference||Single Molecule Spectroscopy and Superresolution Imaging XII 2019|
|Period||2/02/19 → 3/02/19|
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- Localization microscopy
- Multi-channel imaging
- Multi-species imaging
- PSF engineering
- Sparse recovery