AI Article Synopsis
- The text discusses a new super-resolution microscopy technique using phase masks for improved 3D imaging and tracking, addressing the lack of recovery algorithms for complex point spread functions.
- The authors present a 3D recovery algorithm that generates initial guesses through fast deconvolution and refines these guesses using least squares fitting while avoiding overfitting with a machine learning threshold.
- Additionally, the program utilizes parallel computation on GPUs to significantly cut down processing time, making it the first open-source tool for generalized 3D recovery with rotating point spread functions.
Article Abstract
Super-resolution microscopy with phase masks is a promising technique for 3D imaging and tracking. Due to the complexity of the resultant point spread functions, generalized recovery algorithms are still missing. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. A fast deconvolution process generates initial guesses, which are further refined by least squares fitting. Overfitting is suppressed using a machine learning determined threshold. Preliminary results on experimental data show that our algorithm can be used to super-localize 3D adsorption events within a porous polymer film and is useful for evaluating potential phase masks. Finally, we demonstrate that parallel computation on graphics processing units can reduce the processing time required for 3D recovery. Simulations reveal that, through desktop parallelization, the ultimate limit of real-time processing is possible. Our program is the first open source recovery program for generalized 3D recovery using rotating point spread functions.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4973222 | PMC |
| http://dx.doi.org/10.1038/srep30826 | DOI Listing |
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