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8 November 2017 Super-resolution structured illumination in optically thick specimens without fluorescent tagging
Zachary R. Hoffman, Charles A. DiMarzio
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Journal of Biomedical Optics, Vol. 22, Issue 11, 116003 (November 2017). https://doi.org/10.1117/1.JBO.22.11.116003
Abstract
This research extends the work of Hoffman et al. to provide both sectioning and super-resolution using random patterns within thick specimens. Two methods of processing structured illumination in reflectance have been developed without the need for a priori knowledge of either the optical system or the modulation patterns. We explore the use of two deconvolution algorithms that assume either Gaussian or sparse priors. This paper will show that while both methods accomplish their intended objective, the sparse priors method provides superior resolution and contrast against all tested targets, providing anywhere from ∼1.6× to ∼2× resolution enhancement. The methods developed here can reasonably be implemented to work without a priori knowledge about the patterns or point spread function. Further, all experiments are run using an incoherent light source, unknown random modulation patterns, and without the use of fluorescent tagging. These additional modifications are challenging, but the generalization of these methods makes them prime candidates for clinical application, providing super-resolved noninvasive sectioning in vivo.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE) 1083-3668/2017/$25.00 © 2017 SPIE
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Zachary R. Hoffman and Charles A. DiMarzio "Super-resolution structured illumination in optically thick specimens without fluorescent tagging," Journal of Biomedical Optics 22(11), 116003 (8 November 2017). https://doi.org/10.1117/1.JBO.22.11.116003
Received: 24 July 2017; Accepted: 23 October 2017; Published: 8 November 2017
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KEYWORDS
Super resolution
Image processing
Modulation
Point spread functions
Deconvolution
Image resolution
Optical transfer functions
CHORUS Article. This article was made freely available starting 08 November 2018
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