Automated Edge Extraction and High-Clarity Phase Imaging via Pupil-Driven Differential Phase Contrast Imaging

Quantitative differential phase contrast (qDPC) microscopy enables high-resolution, label-free imaging of weakly absorbing samples by combining asymmetrical illumination with phase transfer function (PTF) deconvolution. However, conventional methods are limited by the ill-posed nature of deconvolution and the band-limited characteristics of the PTF, leading to poor robustness against noise and background fluctuations, particularly in thick or complex samples. To overcome these challenges, we propose a pupil-driven differential phase contrast (PD-DPC) framework that integrates system PTFs into both the data fidelity and regularization terms of the reconstruction model. The proposed model incorporates an edge-sparsity-promoting regularization to preserve structural detail and suppress noise, along with a Retinex-inspired fidelity formulation to mitigate background fluctuations. The resulting non-convex optimization problem is solved via an efficient Split Bregman algorithm with iterative reweighted soft-thresholding. Simulations and experiments demonstrate that PD-DPC outperforms L2-DPC, Iso-DPC, TV-DPC, and Retinex-DPC in terms of background suppression, phase fidelity, and edge preservation. The framework is compatible with diverse DPC modalities and enables automatic cell contour segmentation as well as high-resolution imaging of absorbing tissues beyond the weak-object approximation. By combining physics-informed priors with a data-adaptive reconstruction strategy, PD-DPC offers a robust, broadly applicable solution that substantially enhances the accuracy and applicability of qDPC for biomedical imaging. The MATLAB code is available on .