Phase Unwrapping via Deep Learning for Surface Shape Measurement by Using Wavelength-Tuning Interferometry

In the field of optical metrology, wavelength-tunable interferometry is widely used to obtain the phase information of measured objects. Due to the modulo 2π operation, the extracted phase is inherently wrapped into the range of −π to π, which necessitates phase unwrapping to restore the actual phase profile. However, traditional phase-shifting methods suffer from low accuracy caused by phase shift miscalibration, coupling signals, atmospheric turbulence, and measurement noise. To address these issues, this paper proposes a deep learning-based phase-unwrapping method using a deep convolutional neural network, which formulates the unwrapping task as a multiclass classification problem. The proposed method employs an encoder–decoder residual network (ResNet) architecture that treats phase unwrapping as a pixel-wise semantic segmentation task, enabling end-to-end continuous phase reconstruction. It also adopts a 2N − 1 algorithm-based dataset generation strategy that inherently suppresses phase-shift miscalibration and harmonic coupling errors without relying on Zernike polynomial representations. Furthermore, a large-scale data augmentation pipeline (16-fold expansion to 20,992 training samples) endows the network with a strong generalization capability and noise immunity. The quantitative experimental results demonstrate that the proposed method achieves 100% phase-unwrapping accuracy under noise-free conditions and 99.03% accuracy under severe noise (standard deviation = 1.5), substantially outperforming the quality-guided method (QG, 69.87%) and the transport-of-intensity equation method (TIE, 77.53%) under identical conditions. On real interferometric data acquired using a wavelength-tuning interferometer, the proposed method successfully unwraps the phase even under heavy noise where conventional methods fail completely. These results confirm that the proposed method has favorable noise resistance and potential applicability in high-precision optical metrology..