Journal
OPTICS EXPRESS
Volume 26, Issue 11, Pages 14375-14391Publisher
Optica Publishing Group
DOI: 10.1364/OE.26.014375
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Funding
- Natural Science Foundation of Jiangsu, China [BK20161402]
- National Natural Science Foundation of China [91132710, 31671174, 31501133, 31671452]
- National Institutes of Health (NIH) [R01CA175360]
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Conventional deconvolution methods assume that the microscopy system is spatially invariant, introducing considerable errors. We developed a method to more precisely estimate space-variant point-spread functions from sparse measurements. To this end, a space-variant version of deblurring algorithm was developed and combined with a total-variation regularization. Validation with both simulation and real data showed that our PSF model is more accurate than the piecewise-invariant model and the blending model. Comparing with the orthogonal basis decomposition based PSF model, our proposed model also performed with a considerable improvement. We also evaluated the proposed deblurring algorithm. Our newdeblurring algorithm showed a significantly better signal-to-noise ratio and higher image quality than those of the conventional space-invariant algorithm. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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