Polarization Imaging Accuracy via Least Squares Matrix Computation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for acquiring maximum and minimum polarization intensity images in polarization imaging are prone to human error, limiting the accuracy of polarization image processing due to the need for macroscopic observation and inability to distinguish and resolve errors effectively.
Innovation Solution
A method involving a polarizer placed between a detector and a polarization imaging target, using a polarization cosine characterization equation to compute polarized subimages at multiple angles, and employing linear least squares matrix computation to determine linear and non-linear polarization intensity images and polarization angles, thereby improving accuracy and reducing experimental errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional macroscopic observation method is used to rotate polarizer and find maximum and minimum polarization intensity images, then operation is simple, but measurement precision deteriorates due to human error
Solution Approach 1:
The patent replaces the mechanical manual observation and rotation method with an automated computational system. Multiple polarized subimages are captured at different polarization angles, and a parameter matrix is computed using linear least squares matrix computation to automatically determine maximum and minimum polarization intensity images, eliminating human error in macroscopic observation.
Solution Approach 2:
The patent performs preliminary action by capturing multiple polarized subimages at different polarization angles before computing the final maximum and minimum polarization intensity images. This preliminary data collection at multiple angles enables accurate computational determination of extreme values without manual intervention.
2Device complexity
If traditional experimental method is used to acquire polarization images, then device structure is simple, but measurement precision deteriorates due to inability to distinguish and resolve errors
Solution Approach 1:
The patent introduces a parameter matrix as an intermediary computational tool between the captured polarized subimages and the final polarization intensity results. This parameter matrix contains intermediate computational results that allow errors to be distinguished and resolved through mathematical computation, while the physical device structure remains relatively simple.
Solution Approach 2:
The patent replaces manual error identification and resolution processes with automated linear least squares matrix computation. The computational method automatically processes the polarized subimages, computes the parameter matrix, and resolves errors through mathematical operations, maintaining simple device structure while dramatically improving error resolution capability.
3Measurement precision
If computational method with parameter matrix is used to compute maximum and minimum polarization intensity images, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a computational system that uses linear least squares matrix computation to process polarized subimages and compute maximum and minimum polarization intensity images. This computational approach significantly improves measurement precision by automatically determining extreme values from multiple angular measurements, with the complexity confined to the computational algorithm rather than physical device structure.
Solution Approach 2:
The patent performs preliminary computational action by first capturing multiple polarized subimages at different polarization angles and computing a parameter matrix before determining the final maximum and minimum polarization intensity images. This structured computational process breaks down the complex task into manageable steps, improving precision while organizing the computational complexity systematically.
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 method enhances the accuracy of polarization imaging by computing maximum and minimum polarization intensity images with higher precision than traditional methods, effectively reducing the influence of experimental errors and providing a more accurate polarization information standard model for each pixel point.
Implementation Method 1
A polarizer is located between a detector and a polarization imaging target, and is placed close to the detector to cover the full aperture of the detector
Implementation Method 2
n polarized subimages of the polarization imaging target are obtained, where n≥3. Each polarized subimage corresponds to a different polarization angle
Data Source
AI summary
A method for characterizing polarization image information and a method for computing characterization parameters are provided. The method includes: obtaining n polarized subimages of a polarization imaging target, wherein each polarized subimage corresponds to a different polarization angle and n≥3; and computing a parameter matrix [ILP (x, y) INLP(x, y) θ(x, y)] of a polarization cosine characterization equation of all pixel points of the polarization imaging target according to all polarized subimages and the polarization cosine characterization equation of the polarization imaging target. The disclosure obtains a maximum polarization intensity image and a minimum polarization intensity image through computing multiple polarization intensity images, which improves the accuracy of polarization imaging, and the degree of accuracy is much higher than macroscopic accuracy. The disclosure computes polarization cosine characterization parameters through the least squares fitting, which can also effectively reduce the influence of an experimental error on the experimental result.