In the process of colour space conversion from CMYK to CIELab, colour difference will be caused, which has a negative impact on the quality of digital printing products. In this article, an improved wavelet neural network (WNN) model optimised by cuckoo search (CS) algorithm is proposed to reduce the colour difference. Initially, the colour space conversion model based on WNN is established. The CS algorithm is used to optimise the initial weights and parameters of dilation and translation in the WNN model. Then, 1296 samples are made to train the CS‐WNN model. Finally, 100 test samples are input into the trained network to obtain the corresponding L, a and b values of CIELab. The experimental results show that the average conversion colour difference () of the proposed model is 3.469. The conversion accuracy and stability of the proposed model are better than the traditional neural network.

中文翻译：

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基于CS‐WNN的从CMYK到CIELab的色彩空间转换模型

在从CMYK到CIELab的色彩空间转换过程中，会引起色差，这对数字印刷产品的质量有负面影响。本文提出了一种通过杜鹃搜索（CS）算法优化的改进的小波神经网络（WNN）模型，以减少色差。最初，建立了基于WNN的色彩空间转换模型。CS算法用于优化WNN模型中的初始权重和膨胀和平移参数。然后，制作了1296个样本以训练CS-WNN模型。最后，将100个测试样本输入到经过训练的网络中，以获得CIELab的相应L，a和b值。实验结果表明，平均转换色差（）的建议模型是3.469。该模型的转换精度和稳定性均优于传统的神经网络。