High-fidelity lens-free on-chip microscopy via dual-channel noise separation

Lens-free on-chip microscopy (LFOCM) is a high-throughput computational imaging technique that enables high-resolution, label-free imaging without requiring complex optical systems. However, LFOCM encounters significant challenges in achieving high-resolution reconstructions due to noise accumulation. We propose a high-fidelity LFOCM method that integrates pixel super-resolution (PSR) with dynamic dual-channel noise separation (DCNS). This approach simultaneously separates the amplitude and phase noise during the reconstruction process, thereby improving noise robustness and enhancing the dynamic range of quantitative amplitude and phase imaging. Experimental validation across various sample types demonstrated the effectiveness of our method. DCNS achieves a resolution that exceeds 34.1% of the Nyquist-Shannon sampling limit, with a full field of view (FOV) of 28.6 mm², improving the dynamic range of phase reconstruction and effectively suppressing artifacts that degrade the reconstruction quality, thus resolving the trade-off between noise reduction and resolution.