Improve neural network‐based color matching of inkjet textile printing by classification with competitive neural network

Abstract

Nowadays, with increasing use of digital printing in the textile industry, characterization and color matching are very much considered. There is a very complicated relationship between pixel values of input digital image and colorimetric parameters of printed textile samples. One of the most important used methods for inverse characterization of printer and prediction of CMYK digital values is neural network. In this study, the prediction accuracy of CMYK digital values were improved by dividing the training samples into 2, 4, 6, 8, and 10 subgroups using creating a competitive neural network. For classification of samples, L*a*b* or XYZ were introduced to a competitive neural network as input parameters. Then, the classification of test samples was performed by trained competitive neural network. To predict the of CMYK digital values of input digital image, a cascade‐forward back propagation neural network is trained by L*a*b* of each subgroup. The results obtained show that the prediction accuracy of CMYK digital values were improved by suggested method. The best result was obtained by classification of samples with L*a*b* into eight subgroups and using a cascade‐forward back propagation neural network with 4, 4, and 4 neurons in hidden layers.

A Study on DNN-Based Practical Model for Predicting Spot Color

“…Therefore, it can be concluded that the prediction model created in this study can effectively estimate the majority of spot colors. Diving deeper into the color space (Figure 5), we see that the predictions are consistently accurate for darker shades with L* values below 33 However, notable discrepancies were observed in certain predictions, likely due to the model's training process. Because this approach relies primarily on ink mixing ratios without specific color information, this approach may lead to a situation where if certain data patterns are underepresented compared to others, the model might struggle to learn and account for them effectively.…”

“…Littlewood et al [32] presented an ANN model as a function for converting device-independent CIELAB values from digital images to CMYK spaces for printing. Hajipour and Shams-Nateri [33] improved the accuracy of predicting CMYK values for reproducing printed sample colors by introducing a cascade-forward neural network on training samples divided into multiple subgroups by a competitive neural network. Due to the non-uniformity of the LAB color space, Zhao and Chen [34] divided the printed color sample into 10 parts based on hue angle and developed a model that uses a separate neural network for each part to learn the complex relationship between printed colors and digital pixel values (RGB).…”