Multispectral Reflectivity Imaging With Grayscale Region Spectrum Estimation
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Solution Overview
Problem
Existing multispectral imaging technologies struggle to accurately acquire a high-precision spectrum of ambient light, which is crucial for obtaining high-precision multispectral reflectance images, as current methods based on fuzzy predictions of the entire image have weak adaptability and large errors in scenarios with different light sources.
Innovation Solution
A method involving the reconstruction of an RGB image from a multispectral image, conversion to a grayscale image, and identification of a target region with grayscale values less than or equal to a threshold, followed by calculating a light source spectrum response value based on multispectral response values of pixels in that region, using histogram statistics to determine the threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If fuzzy prediction of the entire image is used to estimate light source spectrum, then the method is simple to implement, but the measurement precision of light source spectrum is poor
Solution Approach 1:
The patent divides the image into multiple regions with different grayscale values and selects specific regions (those with grayscale values within a certain range) for light source spectrum estimation. This segmentation approach allows the method to focus on regions that are most representative of the light source characteristics, thereby improving measurement precision while maintaining implementation simplicity.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on their grayscale values. Regions with grayscale values within a specific range are selected for spectrum estimation, while other regions are excluded. This local quality approach ensures that the most suitable regions are used for accurate light source spectrum measurement.
2Quantity of substance
If the entire image is used for light source spectrum estimation, then more data is available for calculation, but the adaptability to different light sources is poor
Solution Approach 1:
The patent segments the image into regions with different grayscale characteristics and selectively uses only those regions whose grayscale values fall within a predetermined range for light source spectrum estimation. This segmentation enables the method to adapt to different light sources by focusing on the most relevant regions rather than using all available data indiscriminately.
Solution Approach 2:
The patent dynamically adjusts the selection of regions based on the grayscale value distribution in the image. By setting the grayscale value range according to the specific imaging conditions and light source characteristics, the method can adapt to different lighting scenarios, making it versatile across various light sources.
3Quantity of substance
If regions with extreme grayscale values are included in spectrum calculation, then more pixels are used for estimation, but the measurement precision decreases
Solution Approach 1:
The patent explicitly excludes regions with extreme grayscale values (both very bright and very dark regions) from the spectrum estimation calculation. By segmenting the image and selecting only regions with grayscale values within a moderate range, the method ensures that the selected pixels provide more reliable and accurate light source spectrum information.
Solution Approach 2:
The patent applies a quality filter to regions based on their grayscale values, selecting only those regions that meet the grayscale criteria for accurate spectrum estimation. This local quality assessment ensures that only pixels from appropriate regions contribute to the light source spectrum calculation, thereby maintaining high measurement precision.
Data Source
AI summary
A light source spectrum acquisition method includes: acquiring a multispectral image, and determining a multispectral response value of each pixel in the multispectral image; reconstructing an RGB image according to the multispectral image; converting the RGB image into a grayscale image; and determining a target region in the grayscale image, wherein in the target region a grayscale value is less than or equal to a threshold, and calculating a light source spectrum response value according to multispectral response values of pixels of the multispectral image corresponding to the target region.