Terahertz Spectral Imaging Data Reconstruction via Region Segmentation
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Solution Overview
Problem
Current terahertz spectral imaging methods require extensive data collection at high spatial and spectral resolutions, resulting in large data volumes and prolonged scanning times, which decreases sampling efficiency and necessitates expensive rapid scanning devices.
Innovation Solution
The method involves scanning objects with terahertz pulses at hyper-spectral resolution at a small fraction of pixel points and low-spectral resolution at a larger fraction, constructing and reconstructing data-cubes to achieve hyper-spatial and hyper-spectral imaging, reducing total data volume and scanning time while maintaining high resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If terahertz spectral imaging is performed at high spatial and spectral resolutions for all pixel points, then imaging quality is improved, but scanning time and data volume increase significantly
Solution Approach 1:
The patent divides the imaging area into multiple regions of interest (ROIs) and processes each region separately with appropriate resolution settings. This segmentation allows high-resolution imaging only where necessary while using lower resolution for other areas, thereby reducing overall scanning time and data volume while maintaining required imaging quality in critical regions.
Solution Approach 2:
The patent applies different spectral and spatial resolution settings to different regions based on their specific requirements. Regions of interest receive high-resolution imaging, while non-critical regions use lower resolution. This local quality approach optimizes the balance between imaging quality and scanning efficiency by matching resolution to actual needs in each local area.
2Measurement precision
If terahertz spectral imaging is performed at high spatial and spectral resolutions for all pixel points, then imaging quality is improved, but data volume increases significantly
Solution Approach 1:
The patent segments the data collection process by region, collecting high-resolution data only for identified regions of interest and lower-resolution data for other areas. This segmentation strategy significantly reduces total data volume while ensuring that critical regions maintain high imaging quality for accurate analysis.
Solution Approach 2:
The patent implements local quality control by assigning different data collection resolutions to different spatial regions based on their importance. This approach minimizes data volume by collecting detailed information only where needed, while reducing or eliminating redundant high-resolution data collection in non-critical regions.
3Measurement precision
If terahertz spectral imaging is performed at high resolution for all pixel points, then sampling efficiency decreases, but imaging completeness is improved
Solution Approach 1:
The patent performs preliminary identification of regions of interest before conducting detailed spectral imaging. By pre-segmenting the imaging area and identifying which regions require high-resolution analysis, the system can then allocate scanning resources efficiently, performing high-resolution imaging only on necessary regions and thereby improving overall sampling efficiency without compromising imaging completeness.
Solution Approach 2:
The patent applies partial action by performing high-resolution spectral imaging only on identified regions of interest rather than on the entire imaging area. This selective approach maintains sampling efficiency by avoiding redundant high-resolution scans in non-critical regions while ensuring complete and accurate imaging of important areas.
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 decreases total scanning time and increases sampling efficiency, allowing for the acquisition of high-resolution spectral images using ordinary scanning devices, while reducing data collection at most pixel points.
Implementation Method 1
scanning an object with a terahertz pulse to generate terahertz spectral data having a hyper-spectral resolution or a low-spectral resolution
Data Source
AI summary
A method for collecting and processing terahertz spectral imaging data includes: scanning an object to generate terahertz spectral data having a hyper- or low-spectral resolution respectively at a plurality of pixel points; collecting the terahertz spectral data and recording coordinate data of each pixel point, thereby obtaining a mixed data including a hyper-spectral data and a low-spectral data corresponding to different pixel points; constructing a hyper-spatial and low-spectral imaging data-cube from the mixed data; extracting a hyper-spectral data-set from the mixed data; and reconstructing a hyper-spatial and hyper-spectral imaging data-cube from the hyper-spatial and low-spectral imaging data-cube based on the hyper-spectral data-set. A terahertz spectral imaging apparatus is further provided.