提出了一种用于数码显微镜的非线性白平衡方法.以标准D65光源光照下孟赛尔色卡每个色块的XYZ三刺激值和所成图像中的RGB值为训练样本，用神经网络建立不同数码显微镜拍摄的RGB输出与XYZ三刺激值之间的关系；再通过Estevens矩阵把XYZ三刺激值转换成人眼感光器所接受到的刺激值，并进行非线性计算;最后把计算结果转换回RGB空间，从而得到白平衡后的图像.实验结果表明，该方法所得的白平衡校正结果更加接近标准白光照射下的图像颜色.在光源色纯度较高的情况下，采用调整通道增益的方法变化幅度较大，而采用本文提出方法结果更加稳定;特别是当光源的色纯度过大使得图像中某个颜色通道的值接近0的时候，用调整三通道增益的方法已经完全失去了校正作用，而采用本文中的非线型方法仍然可以得到好的校正结果.同时，此方法中建立的神经网络模型反映的是成像设备的RGB空间到XYZ空间的转换特性，对于不同的光照条件，此特性不变，因此对于不同光源不需要分别建立神经网络模型.

A new non-linear white balance method is proposed. Taking XYZ stimulus and RGB values of each color block in Munsell color chart illuminated by D65 light source as training samples. A BP neural network mapping relation is built between digit-image RGB values of standard Munsell color chart and XYZ stimulus. Then Estevens matrix is introduced, which is used in translating XYZ stimulus to cone stimulus. After non-linear calculation, the results are translated back into digit-image RGB, values and an image after white balance is produced. It is indicated that this method gives a correct result much closer to image of objects illuminated by standard light sources. In the case of high purity light sources, a large volatility exists in adjusting channel-gains, while a stable result using the proposed method. Especially when the purity of the light source is too high to make values of some color channel approach 0, the channels gain method is out of action, while a good correct result still can be achieved. Meanwhile, the mapping relation between RGB values of digit-image and XYZ stimulus is built, which is a still character for different lighting condition.