Mimicking non-ideal instrument behavior for hologram processing using neural style translation

Area of Science:

• Atmospheric science
• Optics
• Data science

Background:

• Holographic cloud probes offer detailed particle analysis.
• Processing holographic data with ML models is computationally intensive and requires accurate training data.
• Simulated holograms are used for ML training, but real-world imperfections are challenging to replicate.

Purpose of the Study:

• To develop a method for improving ML model performance on holographic cloud probe data.
• To reduce the need for manual labeling and computational resources in processing holographic data.
• To enhance the transferability of ML models trained on simulated data to real-world applications.

Main Methods:

• Applied neural style translation using pre-trained convolutional neural networks to simulated holograms.
• Preserved essential image content (particle size, location) while mimicking real-world noise and imperfections.
• Trained ML models on the stylized simulated holograms.

Main Results:

• Achieved comparable ML model performance on both stylized simulated and real holographic data.
• Eliminated the need for manual labeling of real holographic data for ML training.
• Demonstrated the effectiveness of neural style translation in bridging the gap between simulated and real observational data.

Conclusions:

• Neural style translation offers an efficient method for preparing simulated data for ML applications in fields with observational instruments.
• This approach can generalize to other domains requiring the simulation of instrument noise and imperfections.
• The technique significantly reduces the effort required for data processing and model training in holographic analysis.