Pixel super-resolved lensless on-chip sensor with scattering multiplexing

TL;DR

This paper introduces a novel lensless on-chip sensor using scattering multiplexing and a robust super-resolution algorithm, significantly enhancing resolution, contrast, and noise robustness for large-scale biomedical imaging.

Contribution

It presents an integrated scattering-based modulator and a combined model-driven and data-driven super-resolution algorithm, improving resolution and reducing calibration complexity.

Findings

Enhanced resolution beyond detector limits

Improved image contrast and noise robustness

Reduced calibration and measurement complexity

Abstract

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 to 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 image quality, reconstruction 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 sample reconstruction. The sensor employed a scattering layer as a modulator, which was permanently integrated with the detector. Benefiting from the high-degree-of-freedom reconstruction of the scattering layer, we realized fine wavefront modulation with a small feature size. The integration engineering avoided repetitious calibration and reduce the measurement complexity. The reported PSR algorithm combines both model-driven and data-driven strategies to efficiently exploit the high-frequency information from the fine modulation. A series of experiments validated that the reported sensor provides a low-cost solution for large-scale microscopic imaging, with significant advantages in resolution, image contrast and noise robustness.