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

Yemeng Chen, Mengmeng Chen, Li Zhu, Jane Y. Wu, Sidan Du, Yang Li*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)14375-14391
Number of pages17
JournalOptics Express
Issue number11
StatePublished - May 28 2018

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics


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