Multispectral Sensor Ambient Light Classification
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Solution Overview
Problem
Existing automatic white balancing (AWB) methods in image-sensing devices are limited by their reliance on algorithms like the Gray-World Theory and White Patch Theory, which often produce inconsistent results and fail to accurately adapt to varying ambient light conditions.
Innovation Solution
An image-sensing device equipped with a multispectral sensor and a processor that classifies ambient light sources by comparing predefined spectral data to data from the multispectral sensor, enabling accurate automatic white balancing and adaptation to actual lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing AWB methods (Gray-World Theory or White Patch Theory) are used, then the device complexity is low, but the measurement precision of ambient light source classification deteriorates
Solution Approach 1:
The patent divides the ambient light source classification into multiple discrete spectral categories (e.g., incandescent, fluorescent, LED, daylight) with predefined spectral signatures. The multispectral sensor segments the continuous spectrum into distinct wavelength bands, allowing comparison against predefined spectral profiles for each light source type, thereby improving classification accuracy.
Solution Approach 2:
The patent introduces a multispectral sensor as an intermediary device between the ambient light source and the image sensor. This sensor acts as a mediator that provides detailed spectral information about the illumination, enabling more accurate white balancing without requiring complex post-processing algorithms.
2Adaptability or versatility
If existing AWB methods are used, then the ease of operation is high, but the adaptability to varying ambient light conditions deteriorates
Solution Approach 1:
The patent implements dynamic adaptation by continuously monitoring the spectral characteristics of ambient light and adjusting the white balance parameters in real-time. The system dynamically selects and switches between different predefined spectral profiles based on the detected light source, enabling seamless adaptation to varying lighting conditions without manual intervention.
Solution Approach 2:
The patent changes the operational parameters of the image processing pipeline based on the detected ambient light spectrum. By adjusting white balance gains, color correction matrices, and exposure parameters according to the identified light source type, the system achieves high adaptability while maintaining automated operation.
3Measurement precision
If spectral information is used for classification, then the measurement precision of light source identification is improved, but the use of energy by the device increases
Solution Approach 1:
The patent employs partial spectral measurement by focusing the multispectral sensor on specific wavelength bands that are most discriminative for light source identification. Rather than measuring the entire spectrum with high resolution, the system selectively samples key spectral regions, reducing energy consumption while maintaining accurate classification.
Solution Approach 2:
The patent uses a cost-effective multispectral sensor with limited spectral resolution compared to full-spectrum imaging systems. The sensor provides sufficient spectral information for light source classification without the high energy cost and complexity of more advanced spectral imaging equipment, offering a practical balance between accuracy and energy efficiency.
Data Source
AI summary
An image-sensing device is disclosed, the image-sensing device comprising a multispectral sensor and a processor communicably coupled to the multispectral sensor. The processor is configured to determine an ambient light source classification based on a comparison of predefined spectral data to data corresponding to an output of the multispectral sensor. Also disclosed is a method of classifying an ambient light source by sensing a spectrum of light with a multispectral sensor; and determining an ambient light source classification based on a comparison of predefined spectral data to data corresponding to an output of the multispectral sensor. An associated computer program, computer-readable medium and data processing apparatus are also disclosed.