Background segmentation and aberration fitting via deep learning for accurate phase correction in digital holographic interferometry

Digital holographic interferometry is a high-precision technique for quantitative imaging and measurement. However, the phase retrieved from interference patterns requires correction to obtain the true object phase. Existing numerical methods typically rely on either assumed prior constraints (as in traditional optimization-based approaches) or accurate correction data labels (as in deep learning-based methods), making it difficult to achieve an optimal balance between improving accuracy and reducing dependence on labelled data. To this end, we propose a joint neural network framework comprising a mask neural network and a phase correction neural network (MNN-PCNN) for joint background segmentation and aberration fitting to achieve phase correction in digital holographic interferometry. Inspired by the masked least-squares polynomial fitting, MNN-PCNN first employs a segmentation-based MNN to separate the pure background regions, thereby eliminating interference from the object. Subsequently, PCNN with embedded Zernike polynomial modes is leveraged to achieve accurate and reliable aberration compensation and noise suppression via self-supervised learning. Both simulation and experimental results demonstrate that the proposed MNN-PCNN outperforms classical polynomial fitting methods as well as the representative UNet. Furthermore, the developed network effectively eliminates phase deviations retrieved across multiple projection angles in digital holographic micro-tomography, which plays a critical role in enhancing the accuracy of quantitative three-dimensional measurements.