Spectral Imaging Illumination Mask for Cross-Contamination Reduction
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Solution Overview
Problem
Spectral imaging datasets face challenges such as spectral cross-contamination and intensity inhomogeneities due to topographic features and shadowing effects, which complicate data processing and segmentation, especially in reflection geometry-based measurements.
Innovation Solution
A pre-processing method using an illumination mask extracted from the channel image with maximum reflectance, applied through low-pass filtering in the Fourier domain, corrects intensity variations across the spectral cube without modifying spectral features, ensuring minimal spectral cross-contamination and maintaining data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral imaging is performed in reflection geometry to capture spatial and spectral information, then material detection and classification capability is improved, but spectral cross-contamination and intensity inhomogeneities occur due to topographic features and shadowing effects
Solution Approach 1:
The patent segments the spectral imaging data by identifying and separating regions affected by topographic features and shadowing from regions with clean spectral signatures. This is achieved through spatial segmentation of the imaged scene and spectral segmentation of contaminated versus clean pixels, allowing independent processing of different data regions to eliminate cross-contamination while preserving material detection capability
Solution Approach 2:
The patent extracts and removes the harmful effects of topography and shadowing from the spectral data by identifying contamination patterns and separating them from the true material spectral signatures. This extraction process isolates the contaminating influences so they can be eliminated, leaving only the pure spectral information needed for accurate material detection
2Object-affected harmful factors
If geometric arrangement with significant angle and distance between source and detector is used to prevent direct reflection, then spectral cross-contamination is reduced, but measurement reliability deteriorates due to sample structure and topography becoming obstacles
Solution Approach 1:
The patent creates a computational model or copy of the topographic effects and shadowing patterns observed in the spectral data. By replicating these effects in a controlled computational environment, the system can identify and remove their influence from the measurements, thereby maintaining measurement reliability without requiring complex geometric arrangements that would be obstructed by sample topology
3Productivity
If pre-processing correction is applied to compensate topographic effects, then image segmentation robustness is improved, but spectral features may be modified leading to loss of spectral information
Solution Approach 1:
The patent applies different processing strategies to different regions of the spectral data based on their local characteristics. Regions with severe topographic effects receive targeted correction only where needed, while regions with clean spectra are left unmodified. This localized approach ensures that spectral information is preserved in uncontaminated areas while still improving segmentation robustness in problematic regions
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 allows for robust and consistent image segmentation, reducing the need for spatial region of interest selection and avoiding contamination between regions, while maintaining spectral coherence and eliminating the need for additional measurements or 3D reconstruction, thus enhancing the accuracy of spectral imaging.
Implementation Method 1
A pre-processing method using an illumination mask extracted from the channel image with maximum reflectance, applied through low-pass filtering in the Fourier domain
Data Source
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AI summary
The invention discloses a computer implemented method for modifying spectral imaging for gaining minimum spectral cross-contamination, wherein spectral channel images of a spectral cube of a scene are modified by an illumination mask, wherein the illumination mask is generated by convolutional low-pass filtering of a first spectral channel image of the spectral cube. Furthermore, a corresponding spectroscopy system, a corresponding computer program product, and a corresponding computer-readable storage medium are disclosed.