PSG and PSA Matrix Optimization for Noise Suppression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The accuracy of Müller matrix measurements in polarization imaging is limited by noise and requires improved noise suppression, as existing systems struggle to achieve optimal noise reduction and sample-independent noise distribution.
Innovation Solution
The optimization of Polarization State Generator (PSG) and Polarization State Analyzer (PSA) configurations by adjusting instrumental matrices to minimize equal-weighted variance, ensuring the sum of each row is zero, which optimizes performance against Gaussian-Poisson mixed noise, and utilizing genetic algorithms for optimal configuration determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional PSG and PSA configurations are used for Müller matrix measurement, then the measurement can be performed, but the noise variance is high and measurement accuracy is limited
Solution Approach 1:
The patent optimizes the instrumental matrices W and A by adjusting their parameters (polarization state configurations) to minimize the equal-weighted variance EWV. This involves changing the Stokes vectors of the polarization states to achieve optimal noise suppression while maintaining measurement capability.
Solution Approach 2:
The patent replaces conventional mechanical polarization modulation systems with an optimized mathematical framework using instrumental matrices. Instead of relying on physical rotation of wave plates and polarizers, the system uses computationally optimized matrix representations to achieve superior noise performance.
2Object-affected harmful factors
If instrumental matrices are optimized to minimize equal-weighted variance, then noise suppression is improved, but the configuration complexity increases
Solution Approach 1:
The patent employs genetic algorithms that automatically optimize the instrumental matrices without requiring manual configuration. The algorithm self-adjusts the parameters to minimize EWV, eliminating the need for complex manual setup while achieving optimal noise suppression performance.
Solution Approach 2:
The optimization process uses feedback from the equal-weighted variance calculation to iteratively improve the instrumental matrix configuration. The genetic algorithm evaluates each configuration's noise performance and uses this feedback to guide the search for optimal parameters.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces noise variance, making noise distribution independent of the sample and improving measurement accuracy by minimizing error propagation in Müller matrix measurements.
Implementation Method 1
The Müller matrix Mδ, θ of a phase retardation device is: where is δ a linear phase retardation and θ is an angular direction of a fast axis
Implementation Method 2
Polarization modulation is now mainly achieved by polarizing plate plus one or a series of phase retardation devices, whereby a plurality of different polarization states are obtained by matching different fast axis angles and phase retardation sizes of the phase retardation devices with each other
Data Source
AI summary
A PSG and PSA configuration optimization method and a polarizing and analyzing system, wherein the method comprises the following steps: adjusting an instrumental matrix W of a PSG and an instrumental matrix A of an PSA to minimize a weighted variance EWV of the instrument matrices of the PSG and the PSA, so as to realize optimization for Gaussian noise; and a sum of each row of the instrumental matrix W of the PSG and the instrumental matrix A of the PSA is 0, so that an estimated variance caused by Poisson noise is independent of the sample, and the estimated variance reaches a minimum value. The present disclosure can suppress the noise to the maximum extent and make the law of the noise independent from the sample, and the distribution law of the noise is the same regardless of the sample to be measured.