Phase Unwrapping

Phase unwrapping addresses the problem of reconstructing a continuous phase function from a wrapped, or aliased, phase signal, a common issue in various imaging and sensing modalities. Current research focuses on improving the accuracy and robustness of phase unwrapping techniques, particularly using deep learning architectures like U-Nets and Transformers, as well as hybrid approaches combining deep learning with traditional methods such as Markov Random Fields or integer linear programming. These advancements are crucial for enhancing the accuracy and efficiency of 3D shape measurement, medical imaging (e.g., echocardiography), and other applications relying on phase-based data, ultimately leading to improved data analysis and interpretation.