Phase unwrapping in ICF target interferometric measurement via deep learning.

Area of Science:

• Optics and Photonics
• Machine Learning
• Plasma Physics

Background:

• Phase unwrapping is crucial for analyzing interferometric data in fields like inertial confinement fusion (ICF).
• Traditional phase unwrapping methods can be sensitive to noise and may struggle with complex target geometries.
• Deep learning offers a potential avenue for robust and accurate phase unwrapping.

Purpose of the Study:

• To develop and validate a deep learning-based phase unwrapping algorithm for ICF target interferograms.
• To improve the accuracy and noise resilience of phase unwrapping in ICF measurements.
• To demonstrate the algorithm's generalizability to other optical testing systems.

Main Methods:

• A deep convolutional neural network (CNN) was employed, framing phase unwrapping as a semantic segmentation task.
• A guided filter was used for preprocessing noisy wrapped phase data.
• Postprocessing steps were implemented to refine results and ensure accuracy even with imperfect CNN segmentation.
• A method for generating a dedicated dataset for the ICF target measurement system was developed.

Main Results:

• The proposed deep learning method demonstrated superior accuracy and anti-noise capabilities compared to classical unwrapping algorithms in simulations and on actual interferograms.
• The algorithm successfully unwrapped phases from noisy ICF target interferograms.
• The method showed strong generalization by accurately performing phase unwrapping on an aspheric nonnull test system.

Conclusions:

• The developed deep learning algorithm provides an accurate and robust solution for phase unwrapping in ICF target interferometry.
• The approach offers significant advantages in noise resistance and accuracy over conventional methods.
• The algorithm's adaptability suggests potential transferability to other optical measurement systems with appropriate dataset adjustments.