【摘要】 针对多项式回归颜色校正方法中的不足，提出一种自适应加权的根多项式回归算法。多项式回归颜色校正过程中需要手动标定色卡色块位置，操作不便且易产生人为误差，本文自主设计了一种带QR码上3个定位标识符的色卡，可实现色卡色块的自动定位；针对多项式回归法的高阶项会放大噪声且对噪声不具有健壮性的问题，本文算法将自适应地调整权重矩阵以减小样本奇异值对拟合性能的影响，再由色差值计算另一个增益系数矩阵，从而提高校正性能。经实验验证，本文算法在CIELab色差值、PSNR两项指标上相较于传统多项式回归方法都有较大提升。其中，传统多项式回归方法平均CIELab色差值高达6.5,且该数值受环境影响较大，而本文算法对不同环境下的图像校正后色差可稳定在3.2以下。更多还原

【Abstract】 Aiming at the shortcomings of the polynomial regression color correction method, an adaptive weighted root polynomial regression algorithm is proposed. In the process of polynomial regression color correction, it is necessary to manually calibrate the position of the color block of the color card, which is complicated and prone to human error. In view of the problem that the high-order term of the polynomial will amplify the noise and is not robust to the noise, the algorithm in this paper will adaptively adjust the weight matrix to reduce the influence of singular values on the fitting performance, and then calculate another gain coefficient matrix from the color difference value, thereby improving the correction accuracy. It has been verified by experiments that the algorithm in this paper has a great improvement in the CIELab color difference value and PSNR compared with the traditional polynomial regression method. Among them, the average CIELab chromatic aberration value of the traditional polynomial regression method is as high as 6.5, which is greatly affected by the environment. However, the chromatic aberration value of the proposed algorithm can be stabilized below 3.2 after correcting images in different environments.更多还原