Dual-Frequency Spatio-Temporal Phase Unwrapping

Abstract

Phase unwrapping poses a critical challenge in 3D reconstruction, particularly due to the presence of noise and discontinuities that compromise the accuracy of phase extraction. Existing convolutional neural network (CNN)-based methods have struggled to effectively integrate traditional approaches while fully utilizing both multi-frequency, i.e., temporal, and spatial information of the phase. In this paper, we propose a novel phase unwrapping method, STPhaseNet, which incorporates both temporal and spatial phase information into the CNN framework. Specifically, we introduce a temporal feature fusion module and a local attention mechanism to extract and integrate features from different frequency phases. To further leverage spatial phase information, we develop a spatial information extraction module that enlarges the local receptive field of the convolution and assigns weights based on the phase information of horizontal and vertical coordinates. Additionally, we design a globally optimized gradient residual loss function to exploit spatial constraints more effectively. To address the lack of real-world training data, we apply a Random Matrix Enlargement (RME) method to generate high-quality dual-frequency wrapped phase data along with corresponding absolute phases for training purposes. Extensive experiments demonstrate that STPhaseNet outperforms existing methods, achieving superior performance in phase unwrapping tasks.