Open AccessOptimized sensing of sparse and small targets using lens-free holographic microscopy
Author(s) -
Zhigang Xiong,
Jeffrey E. Melzer,
Jacob Garan,
Euan McLeod
Publication year - 2018
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.26.025676
Subject(s) - regularization (linguistics) , holography , optics , pixel , image resolution , microscopy , resolution (logic) , lens (geology) , digital holographic microscopy , computer science , materials science , physics , artificial intelligence
Lens-free holographic microscopy offers sub-micron resolution over an ultra-large field-of-view >20 mm 2 , 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%.