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

Abstract

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.