Stokes-vector-based polarimetric imaging system for adaptive target/background contrast enhancement.

Area of Science:

• Optics and Photonics
• Computer Vision
• Machine Learning

Background:

• Polarimetric imaging systems face challenges in adaptive target/background contrast enhancement, especially in outdoor environments.
• Optimizing the polarization state is crucial for improving image discriminative capabilities.

Purpose of the Study:

• To propose a novel method for adaptively optimizing the polarization state of a polarimetric imaging system.
• To enhance target/background contrast for improved target discrimination in outdoor settings.

Main Methods:

• Utilized the last three elements of the Stokes vector as polarization features.
• Employed a support vector machine (SVM) method to calculate the optimal polarization state as a physical classifier.
• Trained the system using sample data to determine the optimal polarization state.

Main Results:

• Images processed with the optimal polarization state showed significantly enhanced target/background contrast.
• The proposed method resulted in direct, discriminative output, simplifying target discrimination.
• Experimental validation confirmed the effectiveness and stability of the optimal polarization state.

Conclusions:

• The novel method effectively optimizes polarimetric imaging systems for adaptive contrast enhancement.
• The optimized polarization state significantly improves target discrimination capabilities in outdoor environments.
• The approach offers a credible and stable solution for accurate and convenient target identification.