Transcending conventional snapshot polarimeter performance via neuromorphically adaptive filters

Area of Science:

• Optics and Photonics
• Machine Learning Applications
• Image Processing

Background:

• Channeled Stokes polarimeters are crucial for polarimetric sensing, recovering scene signatures via modulated channels.
• Conventional systems use low-pass filters, which are susceptible to channel crosstalk and require scene-specific optimization.
• This limits their adaptability and accuracy in diverse measurement scenarios.

Purpose of the Study:

• To introduce a novel machine learning-based channel filtering framework for channeled polarimeters.
• To overcome the limitations of conventional filtering methods, such as channel crosstalk and the need for pre-optimization.
• To enhance the accuracy and adaptability of polarimetric signature recovery.

Main Methods:

• A machine learning framework was developed to adaptively predict anti-aliasing filters.
• The machine learning models were trained based on the distribution of measured data.
• A conventional snapshot Stokes polarimeter was simulated to demonstrate the framework's implementation.

Main Results:

• The proposed machine learning framework adaptively generates filters, mitigating channel crosstalk effects.
• Simulations showed the framework's effectiveness in a conventional snapshot Stokes polarimeter setup.
• Reconstructed polarimetric images demonstrated superior performance compared to conventional methods.

Conclusions:

• The machine learning-based channel filtering framework offers a significant advancement for channeled polarimeters.
• This adaptive approach enhances the recovery of polarimetric signatures and reduces sensitivity to scene variations.
• The method provides a more robust and efficient solution for polarimetric sensing applications.