EESANet: edge-enhanced self-attention network for two-dimensional phase unwrapping

In this paper, we first propose a quantitative indicator to measure the amount of prior information contained in the wrapped phase map. Then, Edge-Enhanced Self-Attention Network is proposed for two-dimensional phase unwrapping. EESANet adopts a symmetrical en-decoder architecture and uses self-designed Serried Residual Blocks as its basic block. We add Atrous Spatial Pyramid Pooling and Positional Self-Attention to the network to obtain the long-distance dependency in phase unwrapping, and we further propose Edge-Enhanced Block to enhance the effective edge features of the wrapped phase map. In addition, weighted cross-entropy loss function is employed to overcome the category imbalance problem. Experiments show that our method has higher precision, stronger robustness and better generalization than the state-of-the-art. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

In this paper, a quantitative indicator for measuring the amount of prior information in the wrapped phase map is proposed, followed by the introduction of an Edge-Enhanced Self-Attention Network for two-dimensional phase unwrapping. The network incorporates improved modules such as Atrous Spatial Pyramid Pooling, Positional Self-Attention, and Edge-Enhanced Block to enhance the performance of phase unwrapping. Experimental results demonstrate that the proposed method outperforms the state-of-the-art in terms of precision, robustness, and generalization.