Multispectral Image Conversion Using Reference Vectors for RGB Compatibility
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Solution Overview
Problem
Multi-spectral images require high-capacity memory and storage, cannot be directly displayed on common RGB monitors, and existing software cannot process them, leading to inefficient data handling and processing times due to their larger data size and information amount.
Innovation Solution
An image processing device that converts multi-spectral images into 3-channel images using user-set or automatically determined reference vectors, allowing for reduced data size, compatibility with RGB monitors and software, and high-speed processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-spectral images are stored and processed directly, then spectral information and wavelength resolution are preserved, but data size becomes much larger requiring high-capacity memory and storage
Solution Approach 1:
The patent extracts only the essential spectral information needed for the specific application by projecting multi-spectral data onto a reduced set of basis vectors. This extraction process removes redundant information while preserving the critical spectral characteristics required for analysis, thereby reducing data size without sacrificing measurement precision.
Solution Approach 2:
The patent transforms the data representation from N spectral bands to 3 principal components through mathematical projection. This parameter change reduces the dimensionality of the data from N dimensions to 3 dimensions, significantly decreasing storage requirements while maintaining the ability to retrieve essential spectral information when needed.
2Measurement precision
If multi-spectral images are stored and processed directly, then complete spectral information is retained, but processing time increases due to larger data size
Solution Approach 1:
The patent extracts only the essential spectral information needed for the specific application by projecting multi-spectral data onto a reduced set of basis vectors. This extraction process removes redundant information while preserving the critical spectral characteristics required for analysis, thereby reducing data size without sacrificing measurement precision.
Solution Approach 2:
The patent performs preliminary dimensionality reduction by projecting multi-spectral images onto 3 principal components before subsequent processing operations. This preliminary action simplifies the data structure in advance, making subsequent processing operations faster while the compressed representation retains sufficient information for accurate spectral analysis.
3Adaptability or versatility
If multi-spectral images are used, then wavelength region information is expanded beyond RGB, but compatibility with common displays and software is lost
Solution Approach 1:
The patent introduces 3 principal components as an intermediary representation between the multi-spectral data and standard RGB displays. These principal components serve as a bridge that preserves the expanded wavelength region information from multi-spectral imaging while translating it into a format compatible with common RGB displays and existing software applications.
Solution Approach 2:
The patent transforms the data representation from N spectral bands to 3 principal components through mathematical projection. This parameter change reduces the dimensionality of the data from N dimensions to 3 dimensions, significantly decreasing storage requirements while maintaining the ability to retrieve essential spectral information when needed.
4Measurement precision
If multi-spectral images with N bands are processed, then detailed spectral information is obtained, but the number of parameters to specify increases making processing difficult
Solution Approach 1:
The patent transforms the data representation from N spectral bands to 3 principal components through mathematical projection. This parameter change reduces the dimensionality of the data from N dimensions to 3 dimensions, significantly decreasing storage requirements while maintaining the ability to retrieve essential spectral information when needed.
Data Source
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AI summary
This image processing device has: an image input means for inputting a multi-spectral image which comprises N-number (N is an integer of 4 or more) of channels corresponding to N-number of bands; a reference vector setting means for causing a user to set, as three reference vectors, three types of N-order vectors having, as elements, sensitivities of the respective N-number of bands; and a conversion means for converting the multi-spectral image into a 3-channel image comprising three channels by decomposing a spectral distribution of each pixel of the multi-spectral image on the basis of the three reference vectors.