Pupil-driven quantitative differential phase contrast imaging

TL;DR

This paper introduces a novel pupil-driven qDPC reconstruction algorithm that enhances phase imaging quality by leveraging edge detection properties, improving robustness and efficiency without altering optical systems.

Contribution

The paper proposes a new qDPC reconstruction method using a modified L0-norm and split Bregman algorithms, demonstrating superior performance over existing techniques.

Findings

Enhanced phase reconstruction quality and robustness.

Significant improvement in implementation efficiency.

Effective without modifying optical system components.

Abstract

In this research, we reveal the inborn but hitherto ignored properties of quantitative differential phase contrast (qDPC) imaging: the phase transfer function being an edge detection filter. Inspired by this, we highlighted the duality of qDPC between optics and pattern recognition, and propose a simple and effective qDPC reconstruction algorithm, termed Pupil-Driven qDPC (pd-qDPC), to facilitate the phase reconstruction quality for the family of qDPC-based phase reconstruction algorithms. We formed a new cost function in which modified L0-norm was used to represent the pupil-driven edge sparsity, and the qDPC convolution operator is duplicated in the data fidelity term to achieve automatic background removal. Further, we developed the iterative reweighted soft-threshold algorithms based on split Bregman method to solve this modified L0-norm problem. We tested pd-qDPC on both simulated and experimental data and compare against state-of-the-art (SOTA) methods including L2-norm, total variation regularization (TV-qDPC), isotropic-qDPC, and Retinex qDPC algorithms. Results show that our proposed model is superior in terms of phase reconstruction quality and implementation efficiency, in which it significantly increases the experimental robustness while maintaining the data fidelity. In general, the pd-qDPC enables the high-quality qDPC reconstruction without any modification of the optical system. It simplifies the system complexity and benefits the qDPC community and beyond including but not limited to cell segmentation and PTF learning based on the edge filtering property.