EPUN: Efficient 2D Phase Unwrapping with a Lightweight Neural Network
Year: 2025
Type: article
Abstract: Phase unwrapping is a fundamental task. In recent years, deep learning has been widely used for phase unwrapping in different fields. Nevertheless, along with the unwrapping performance improvement of deep networks, their structures become more and more complicated and diverse. As a result, these designed networks make it difficult to achieve an optimal balance between efficiency and effectiveness. For this purpose, we proposed a lightweight neural network for efficient 2d phase unwrapping (EPUN). EPUN first introduces a feature modulation block to efficiently aggregate long-range spatial context while executing channel mixing on the features. In addition, we design a feature supervision module exclusively used during training, which provides explicit unwrapping supervision for deep-level features, facilitating the network to converge to a better parameter set for optimized phase unwrapping performance. We evaluate EPUN qualitatively and quantitatively on both simulation and real data. The experimental results show that EPUN not only achieves optimal unwrapping performance but also has excellent unwrapping speed.
Source: IEEE Access
Authors: Zhiliang Zhang, Fang Luo, Ying Liu, Yin Zhang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4414404548
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