Multispectral Image Sensor Correction for Mixed-Light Color Accuracy
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Solution Overview
Problem
Existing RGB image sensors struggle to accurately distinguish and correct images based on illumination sources with different spectral characteristics, as they rely solely on color temperature estimation, which is insufficient for accurately rendering object colors under varying lighting conditions.
Innovation Solution
A multispectral image sensor with eight or more channels is used to acquire images, along with a processor that estimates illumination spectral data and performs lens shading correction using a correction table and machine learning, enabling precise illumination source identification and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an RGB image sensor with three channels is used, then the device complexity is low, but the measurement precision of illumination spectral data is insufficient
Solution Approach 1:
The spectrum is segmented into multiple discrete wavelength bands, with each band captured by a dedicated channel in the multispectral image sensor. This segmentation allows precise measurement of spectral characteristics at different wavelengths, resolving the contradiction between measurement precision and device complexity by providing detailed spectral information through structured channel division.
Solution Approach 2:
The patent transitions from three-channel RGB color space to eight-or-more-channel spectral space, adding dimensional depth to the measurement system. This dimensional expansion enables capture of spectral information across multiple wavelength bands, significantly improving illumination spectral data estimation accuracy while maintaining manageable device complexity through systematic channel architecture.
2Reliability
If color temperature estimation alone is used, then the ease of operation is high, but the reliability of illumination source distinction is insufficient
Solution Approach 1:
The patent introduces an illumination spectral data estimation module as an intermediary between image acquisition and correction processing. This module uses machine learning models trained on spectral characteristics to identify and distinguish different illumination sources, providing reliable illumination source distinction while managing processing complexity through pre-trained models and systematic correction workflows.
Solution Approach 2:
The system performs preliminary estimation of illumination spectral data and identification of illumination sources before executing image correction operations. By pre-processing spectral analysis and storing correction parameters, the system ensures reliable illumination source distinction while optimizing processing efficiency, resolving the contradiction between reliability and processing complexity.
3Manufacturing precision
If standard lens shading correction is applied without spectral consideration, then the ease of operation is high, but the manufacturing precision of color accuracy is insufficient
Solution Approach 1:
The patent implements local quality correction by applying wavelength-specific lens shading correction parameters to different spectral channels. Each channel receives customized correction based on its spectral characteristics and the estimated illumination spectrum, ensuring high color accuracy while managing system complexity through channel-specific processing rather than uniform correction.
Solution Approach 2:
The system dynamically adjusts correction parameters based on estimated illumination spectral data. By changing correction parameters according to the specific illumination source and spectral conditions, the system achieves high color accuracy across varying lighting environments while maintaining manageable complexity through adaptive parameter adjustment rather than fixed correction tables.
Data Source
AI summary
An image acquisition apparatus may include a multispectral image sensor configured to acquire an image of at least one object in an environment in which at least one illumination source exists, through eight or more channels with minimum overlap between the channels, and a processor configured to estimate illumination spectral data of the acquired image by using channel signals corresponding to the eight or more channels, and perform lens shading correction on the acquired image, based on the estimated illumination spectral data.