Pixel Super-Resolved Lensless on-Chip Sensor with Scattering Multiplexing

Lensless on-chip microscopy has shown great potential for biomedical imaging due to its large-area and high-throughput imaging capabilities. By combining the pixel super-resolution (PSR) technique, it can improve the resolution beyond the limit of the imaging detector. However, existing PSR techniques are restricted by the feature size and crosstalk of modulation components (such as spatial light modulator), which cannot efficiently encode target information. Besides, the reconstruction algorithms suffer from the trade-off between reconstruction quality, imaging resolution, and computational efficiency. In this work, we constructed a novel integrated lensless on-chip sensor via scattering multiplexing and reported a robust PSR algorithm for target reconstruction. We employed a scattering layer to replace conventional modulators and permanently integrated it with the image detector. Benefiting from the high-degree-of-freedom calibration, the scattering layer realized fine wavefront modulation with a small feature size. Besides, the integration engineering avoided repetitious calibration and reduced the complexity of data acquisition. The reported PSR algorithm combined both model-driven and data-driven strategies, with the advantages of high fidelity, strong generalization, and low computational complexity. The remarkable performance allows it to efficiently exploit the high-frequency information from the fine modulation. A series of experiments validate that the reported sensor and PSR algorithm provide a low-cost solution for large-scale microscopic imaging, realizing ∼1.1 μm imaging resolution and ∼7 dB enhancement on the PSNR index compared to existing methods

Single-Photon-Camera-Based Time and Spatially Resolved Electroluminescence Spectroscopy for Micro-LED Analysis

To investigate the operational mechanisms of micrometer-sized light-emitting diodes (micro-LEDs), we here demonstrate a transient methodology of time and spatially resolved electroluminescence spectroscopy (TSR-EL) to measure the spatial distribution of light emission from LED devices. By combining a single-photon camera (SPC) with the time-gated sampling method, we derived the time and spatially resolved electroluminescence intensity with increasing time. Benefiting from the high sensitivity of the SPC, this methodology can detect ultralow electroluminescence (EL) at the delay stage from the device operated around the turn-on voltage. Furthermore, we investigated the spatial light distribution of a typical quantum dots light-emitting diode (QLED) under different applied voltages and varied temperatures. It was found that the EL emission of the QLED device became more uniform with increasing temperature and applied voltage. Moreover, the methodology of TSR-EL is versatile to investigate other LEDs such as organic light-emitting diodes (OLEDs), micro-LEDs, etc

Supplementary document for Self-supervised learning for single-pixel imaging via dual-domain constraints - 6305894.pdf

Supplementary material for SSUP-SP

Visualization 2: Angular light modulator using optical blinds

Visualization 2 Originally published in Optics Express on 12 December 2016 (oe-24-25-28467

Visualization 1: Angular light modulator using optical blinds

Visualization 1 Originally published in Optics Express on 12 December 2016 (oe-24-25-28467

Odds Ratios (ORs) for ICAS by the Number of Ideal Cardiovascular Health Metrics.

*<p>Model 1: Adjusted for sex and age (year).</p>†<p>Model 2: Adjusted for sex, age (year), education, average monthly income of every family member, and family history of stroke.</p>‡<p>Adjusted for sex, education, average monthly income of every family member, and family history of stroke.</p>§<p>Adjusted for age (year), education, average monthly income of every family member, and family history of stroke.</p

Odds Ratios (ORs) with 95% CI of Ideal to Non-ideal Group of Each Cardiovascular Health Metric for ICAS^*.

<p>CI: confidence interval; ICAS: intracranial artery stenosis; BMI: body mass index.</p>*<p>The following potential confounders were adjusted for each OR: sex, age (year), education, average monthly income of the family members, family history of stroke, and the other six cardiovascular health metrics.</p

Basic Characteristics of Participants regarding the Number of Ideal Cardiovascular Health Metrics.

<p>Note: *Average monthly income of every family member.</p