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Abstract
Phase unwrapping plays a pivotal role in optics and is a key step in obtaining phase information. Recently, owing to the rapid development of artificial intelligence, a series of deep-learning-based phase-unwrapping methods has garnered considerable attention. Among these, a representative deep-learning model called ${{\rm U}^2}$-net has shown potential for various phase-unwrapping applications. This study proposes a ${{\rm U}^2}$-net-based phase-unwrapping model to explore the performance differences between the ${{\rm U}^2}$-net and U-net. To this end, first, the U-net, ${{\rm U}^2}$-net, and ${{\rm U}^2}$-net-lite models are trained simultaneously, then their prediction accuracy, noise resistance, generalization capability, and model weight size are compared. The results show that the ${{\rm U}^2}$-net model outperformed the U-net model. In particular, the ${{\rm U}^2}$-net-lite model achieved the same performance as that of the ${{\rm U}^2}$-net model while reducing the model weight size to 6.8% of the original ${{\rm U}^2}$-net model, thereby realizing a lightweight model.
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