Measure and model a 3-D space-variant PSF for fluorescence microscopy image deblurring

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 w...

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Bibliographic Details
Published in:Optics express 2018-05, Vol.26 (11), p.14375-14391
Main Authors: Chen, Yemeng, Chen, Mengmeng, Zhu, Li, Wu, Jane Y, Du, Sidan, Li, Yang
Format: Article
Language:English
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Summary: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 new deblurring algorithm showed a significantly better signal-to-noise ratio and higher image quality than those of the conventional space-invariant algorithm.
ISSN:1094-4087
1094-4087
DOI:10.1364/OE.26.014375