Bayesian-based aberration correction and numerical diffraction for improved lensfree on-chip microscopy of biological specimens

TL;DR

This paper presents a Bayesian-based approach to correct aberrations and perform numerical diffraction in lensfree on-chip microscopy, significantly enhancing image quality and resolution for biological specimens.

Contribution

It introduces a novel Bayesian method that jointly addresses aberration correction, noise reduction, and diffraction, improving imaging performance over existing techniques.

Findings

Achieved ~25% increase in numerical aperture (NA).

Enhanced signal-to-noise ratio (SNR) by 2.3 to 3.8 dB.

Validated improvements using both resolution targets and biological samples.

Abstract

Lensfree on-chip microscopy is an emerging imaging technique that can be used to visualize and study biological specimens without the need for imaging lens systems. Important issues that can limit the performance of lensfree on-chip microscopy include interferometric aberrations, acquisition noise, and image reconstruction artifacts. In this study, we introduce a Bayesian-based method for performing aberration correction and numerical diffraction that accounts for all three of these issues to improve the effective numerical aperture (NA) and signal-to-noise ratio (SNR) of the reconstructed microscopic image. The proposed method was experimentally validated using the USAF resolution target as well as real waterborne Anabaena flos-aquae samples, demonstrating improvements in NA by ~25% over the standard method, and improvements in SNR of 2.3 dB and 3.8 dB in the reconstructed image when compared to the reconstructed images produced using the standard method and a maximum likelihood estimation method, respectively.