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Computational polarimetric holography for efficient microplastic classification via a lightweight wavelet-enhanced
Optics Express
|May 4, 2026
Summary
This study introduces a portable holographic polarimetric microplastic imager and a lightweight AI model for efficient, in-situ marine microplastic detection. The system achieves high accuracy, enabling real-time environmental monitoring.
Area of Science:
- Environmental Science
- Optical Engineering
- Data Science
Background:
- Marine microplastic pollution requires efficient in-situ detection methods.
- Current lab-based techniques are accurate but costly and time-consuming.
- Portable systems offer a solution, but require computationally efficient models.
Purpose of the Study:
- To develop a high-throughput, in-situ system for multi-class microplastic detection.
- To create a lightweight model for real-time microplastic classification on edge devices.
- To bypass complex reconstruction by directly analyzing polarimetric interference fringes.
Main Methods:
- Developed a portable holographic polarimetric microplastic imager (HPM imager).
- Created WMViT3, a lightweight feature extraction model integrating wavelet transform for fringe analysis.
- Directly decoded raw polarimetric interference fringes to extract material-specific optical fingerprints.
Main Results:
- Achieved 97.97% classification accuracy on the HPM-500 dataset.
- Reduced computational load by 53.2% compared to traditional methods.
- Demonstrated real-time inference capability on embedded devices.
Conclusions:
- The HPM imager and WMViT3 model provide a viable technical solution for portable, real-time marine microplastic monitoring.
- This approach significantly enhances the efficiency and accessibility of in-situ microplastic detection.
- Enables high-throughput analysis crucial for effective environmental monitoring.
