Zhang Ziheng, Wang Xiaoxu, Liu Chengxiu, Han Ziyu, Xiao Qingxiong, Zhang Zhilin, Feng Wenlu, Liu Mingyong, Lu Qianbo
Opt Express. 2024 Apr 22;32(9):15410-15432. doi: 10.1364/OE.517676.
Phase unwrapping is a crucial step in obtaining the final physical information in the field of optical metrology. Although good at dealing with phase with discontinuity and noise, most deep learning-based spatial phase unwrapping methods suffer from the complex model and unsatisfactory performance, partially due to simple noise type for training datasets and limited interpretability. This paper proposes a highly efficient and robust spatial phase unwrapping method based on an improved SegFormer network, SFNet. The SFNet structure uses a hierarchical encoder without positional encoding and a decoder based on a lightweight fully connected multilayer perceptron. The proposed method utilizes the self-attention mechanism of the Transformer to better capture the global relationship of phase changes and reduce errors in the phase unwrapping process. It has a lower parameter count, speeding up the phase unwrapping. The network is trained on a simulated dataset containing various types of noise and phase discontinuity. This paper compares the proposed method with several state-of-the-art deep learning-based and traditional methods in terms of important evaluation indices, such as RMSE and PFS, highlighting its structural stability, robustness to noise, and generalization.
相位展开是光学计量领域获取最终物理信息的关键步骤。尽管大多数基于深度学习的空间相位展开方法善于处理具有不连续性和噪声的相位，但它们存在模型复杂和性能不尽人意的问题，部分原因是训练数据集的噪声类型单一以及可解释性有限。本文提出了一种基于改进的SegFormer网络SFNet的高效且稳健的空间相位展开方法。SFNet结构采用了无位置编码的分层编码器和基于轻量级全连接多层感知器的解码器。所提方法利用Transformer的自注意力机制更好地捕捉相位变化的全局关系，并减少相位展开过程中的误差。它具有较少的参数数量，加快了相位展开的速度。该网络在包含各种类型噪声和相位不连续性的模拟数据集上进行训练。本文在RMSE和PFS等重要评估指标方面，将所提方法与几种基于深度学习的先进方法和传统方法进行了比较，突出了其结构稳定性、对噪声的鲁棒性和泛化能力。
