A high-precision plate color restoration method and system based on color card correction and residual regression

By combining color card calibration and residual regression with global color correction and a lightweight machine learning model, the problem of insufficient color correction accuracy of board materials caused by material differences is solved, achieving high-precision and computationally efficient color restoration, which is suitable for industrial visual inspection and printing management.

CN122415758APending Publication Date: 2026-07-17
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Filing Date
2026-06-12
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing technologies lack the accuracy for color correction of boards due to material differences. Traditional color card correction methods are limited by material differences, while deep learning methods are computationally complex and require large amounts of data, making them difficult to apply in practice.

Method used

A method based on color card correction and residual regression is adopted. By using global color correction and a lightweight machine learning model to compensate for material differences, and combining adaptive multinomial regression and residual prediction model, high-precision color reproduction is achieved.

Benefits of technology

It significantly improves the color reproduction accuracy of the board material, has high computational efficiency, is suitable for resource-constrained equipment, is highly adaptable, and is applicable to industrial online inspection and printed matter management.

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Abstract

本发明公开了一种基于色卡校正与残差回归的颜色高精度还原方法及系统,属于图像处理、颜色科学与机器学习技术领域。该方法包括:获取包含标准色卡和待测物体的原始图像;基于标准色卡的颜色信息,对原始图像进行全局颜色校正,得到校正图像;从校正图像中分割出待测物体的目标区域,并提取目标区域的颜色特征;基于目标区域的颜色特征以及标准色卡在全局颜色校正前后的颜色变化信息,通过预训练的残差预测模型,获取所述待测物体的最终颜色还原值。本发明解决现有技术中因材质差异导致的校正精度不足的问题,同时兼顾计算效率与模型可解释性,可广泛应用于木材在线分选、工业视觉检测、印刷品色彩管理等需要精确颜色还原的场景。
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