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U-ResNet-ESPI: a physics-informed deep learning framework for robust phase unwrapping in electronic speckle pattern
Optics Express
|June 11, 2026
Summary
A new deep learning method, U-ResNet-ESPI, enhances electronic speckle pattern interferometry (ESPI) by improving phase recovery accuracy. This robust technique overcomes severe speckle noise, leading to more reliable measurements.
Area of Science:
- Optics and Photonics
- Metrology
- Artificial Intelligence in Imaging
Background:
- Electronic Speckle Pattern Interferometry (ESPI) is a powerful non-contact measurement technique.
- Phase unwrapping is critical for ESPI precision but is often degraded by speckle noise.
- Conventional algorithms struggle with phase continuity under high noise levels.
Purpose of the Study:
- To develop a noise-robust deep learning architecture for accurate phase recovery in ESPI.
- To enhance the reliability of ESPI measurements in the presence of significant speckle noise.
- To reduce the domain gap between synthetic and real-world ESPI data.
Main Methods:
- Proposed U-ResNet-ESPI, a deep learning architecture integrating physics-driven data synthesis.
- Implemented a physics-aware multi-view preprocessing step.
- Utilized an optimized U-Net with a topology-aware hybrid loss function.
Main Results:
- Achieved highly accurate phase recovery even under severe speckle noise conditions.
- Demonstrated a low error level of only 0.01 radians.
- Significantly outperformed traditional phase unwrapping algorithms in robustness and accuracy.
Conclusions:
- U-ResNet-ESPI offers a robust solution for accurate phase recovery in ESPI.
- The method enhances the reliability of ESPI measurements, particularly in noisy environments.
- The approach is extensible to other coherent imaging techniques like digital holography and stereography.
