Optimized sensing of sparse and small targets using lens-free holographic microscopy
AffiliationUniv Arizona, Coll Opt Sci
MetadataShow full item record
PublisherOPTICAL SOC AMER
CitationZhen Xiong, Jeffrey E. Melzer, Jacob Garan, and Euan McLeod, "Optimized sensing of sparse and small targets using lens-free holographic microscopy," Opt. Express 26, 25676-25692 (2018)
Rights© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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AbstractLens-free holographic microscopy offers sub-micron resolution over an ultra-large field-of-view >20 mm2, making it suitable for bio-sensing applications that require the detection of small targets at low concentrations. Various pixel super-resolution techniques have been shown to enhance resolution and boost signal-to-noise ratio (SNR) by combining multiple partially-redundant low-resolution frames. However, it has been unclear which technique performs best for small-target sensing. Here, we quantitatively compare SNR and resolution in experiments using no regularization, cardinal-neighbor regularization, and a novel implementation of sparsity-promoting regularization that uses analytically-calculated gradients from Bayer-pattern image sensors. We find that sparsity-promoting regularization enhances the SNR by ~8 dB compared to the other methods when imaging micron-scale beads with surface coverages up to ~4%.
NoteOpen access journal
VersionFinal published version
SponsorsUniversity of Arizona