Optimized sensing of sparse and small targets using lens-free holographic microscopy

Zhen Xiong, Jeffrey E. Melzer, Jacob Garan, Euan McLeod

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Lens-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%.

Original languageEnglish (US)
Pages (from-to)25676-25692
Number of pages17
JournalOptics Express
Issue number20
StatePublished - Oct 1 2018

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics


Dive into the research topics of 'Optimized sensing of sparse and small targets using lens-free holographic microscopy'. Together they form a unique fingerprint.

Cite this