Quantitative Phase Imaging From Single‐Direction Gradients Based on U‐KAN and Orthogonal Shear Learning

ABSTRACT Differential interference contrast microscopy (DIC) plays an irreplaceable role in live‐cell dynamic studies due to its non‐destructive, high‐contrast, and 3D imaging capabilities. However, traditional DIC captures only single‐direction gradients, causing orthogonal gradients loss and limiting quantitative phase imaging and anisotropy analysis. Here, we propose an orthogonal shear learning U‐KAN (OSLU‐KAN) architecture for single‐direction phase gradient‐based quantitative phase imaging. This method integrates highly interpretable Kolmogorov‐Arnold networks (KAN) into the U‐Net framework, efficiently learning to predict orthogonal phase gradients from single‐direction gradients. By combining a physics‐driven spiral phase integration (SPI) model and a highly compatible Fourier loss function, this method achieves fast, high‐precision, and artifact‐free phase reconstruction. Experimental results show an RMSE of 0.913 mrad/µm for orthogonal gradient prediction and 0.0103 rad for phase reconstruction. Importantly, OSLU‐KAN enables accurate phase retrieval and anisotropic phase gradients estimation, with excellent compatibility and generalization capabilities, providing a new interpretable, physics‐informed paradigm for deep learning‐driven quantitative phase imaging.