Frequency-domain learning-driven lightweight phase recovery method for in-line holography

Retrieving phase information from a single-intensity image poses a highly ill-posed inverse problem in the field of optical imaging, particularly in applications such as in-line holography, where phase information is critical for accurate reconstruction. Traditional methods exhibit limited applicability in dynamic scenes and struggle to ensure consistent reconstruction quality. Recent advancements in deep learning and the emergence of physics-informed methods have introduced novel strategies to address this challenge. However, despite reduced reliance on extensive datasets, the complexity of network architectures remains a significant barrier. In this study, we propose a frequency-domain learning-driven lightweight phase recovery method (FNet) based on complex-valued networks. By analyzing the characteristics of the optical diffraction process in the frequency domain, we design models with fewer parameters through frequency-domain learning. This lightweight approach effectively minimizes computational resource demands, facilitating efficient phase recovery in resource-constrained environments. Simulation and experimental results on multiple in-line holography datasets demonstrate that FNet achieves performance comparable to conventional methods and real-valued networks while utilizing significantly fewer parameters and computational resources, thereby validating its efficacy. Furthermore, the incorporation of complex-valued total variation regularization markedly reduces artifacts and enhances reconstruction quality in complex datasets. We contend that this work highlights the necessity for alignment between neural networks and physical models, thereby improving operational efficiency and expanding the applicability of phase recovery technologies.