Color Correction Model Using Precomputed Chart Coefficients
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Solution Overview
Problem
Conventional color correction methods using a color chart require capturing an image of the chart with the subject every time, leading to complexity and inefficiency.
Innovation Solution
A trained model generation device and method that calculates and applies correction coefficients to subject images without requiring simultaneous capture of a color chart, using machine learning to generate a trained model for color correction based on learning images that include both the subject and a color chart.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a color chart is captured together with the subject every time, then color correction accuracy is improved, but operation complexity and time consumption increase
Solution Approach 1:
The system performs preliminary action by capturing the color chart separately before actual subject imaging, and pre-calculating correction coefficients. This allows the correction data to be ready in advance, eliminating the need to capture the color chart together with the subject during actual operation, thus reducing operational complexity while maintaining color correction accuracy
Solution Approach 2:
The system creates a copy of the color chart image and processes it separately to generate correction coefficients. This copied color chart data is then applied to correct the subject image, allowing the color reference to be captured and processed independently without requiring simultaneous capture with the subject
2Measurement precision
If a color chart is captured together with the subject every time, then color correction accuracy is improved, but processing time increases
Solution Approach 1:
The correction coefficients are calculated in advance from the separately captured color chart before the actual subject imaging takes place. This preliminary calculation of correction data eliminates the time-consuming process of capturing and processing the color chart together with the subject during actual operation, thus reducing processing time while maintaining color correction accuracy
Solution Approach 2:
The overall color correction process is segmented into separate steps: (1) capturing the color chart separately, (2) calculating correction coefficients from the color chart, (3) capturing the subject image, and (4) applying the pre-calculated coefficients. This segmentation allows the color chart processing to be done independently and in advance, reducing the time required during actual subject imaging
Data Source
AI summary
An information processing device acquires a learning image in which a subject for learning and a color chart appear. The information processing device calculates a correction coefficient for correcting a color appearing in the color chart in the learning image to a reference color as a reference. The information processing device generates learning data in which an image of a portion of the subject for learning in the learning image is associated with the correction coefficient. The information processing device generates a trained model that outputs a correction coefficient for correcting a color of an image in which a subject appears in response to an input of the image based on the learning data.