Solid ink density is an important control parameter in the manufacturing process of offset prints—the size of which has a significant impact on the color performance of the prints—in which the determination of the optimal solid ink density is critical for the pre-press phase of industrial production. Compared with the traditional method of determining the optimal solid ink density, the current printing equipment used to determine the optimal solid ink density will be faster at improving industrial production efficiency and product quality. To improve the efficiency of determining the optimal solid ink density, the Random Forest algorithm was applied for the first time to the prediction task of solid ink density in offset printing. An optimal solid ink density prediction model for lithographic offset printing is established, and the L*a*b* colorimetric values of CMY three-color prints are used as inputs for training through hyperparameter optimization of the model. The experimental data show that the relevant evaluation metrics MAE, RMSE, MSE, and R² of the model are within the reliable range. A comparison between the proposed prediction model and several mainstream machine-learning algorithms indicates that the Random Forest model performs superiorly in both the coefficient of determination (R²) and the mean squared error (MSE). Specifically, the Random Forest model achieved an R² value of 0.969, reflecting improvements of 27.5%, 1.89%, 3.8%, and 34.02% compared to artificial neural network, gradient boosting, polynomial regression, and support vector regression models, respectively. In terms of MSE, the model reduced prediction error by 87.1%, 36.2%, 55.4%, and 89.2%, respectively, when compared with the same models. This approach has proven to provide both theoretical support and a practical pathway for enhancing the level of intelligence in pre-press process control, demonstrating significant practical application value.

Different environmental light affects the effect of textile surface color rendering, thus affecting the accuracy of machine vision-based textile quality inspection. In order to solve this problem, we propose an illumination correction model based on the improved Aquila Optimizer (AO) for the kernel extreme learning machine (KELM). First, a simulated annealing algorithm is used, and its better solution is used as the initial population of the AO, which increases the global search capability and improves the stability of the algorithm. Second, the KELM model is introduced and the AO optimization algorithm is used to obtain the optimal penalty parameter C and kernel parameter γ of the KELM, thus constituting the SA-AO-KELM model. Finally, some textile images are selected to test the effect of the constructed light correction model using their chromaticity features, and the effect of light in the image is eliminated according to the model output. In order to verify the effectiveness of the designed illumination correction method, this paper adopts the chromatic aberration detection for the comparative analysis of the images before and after correction to evaluate the effect of illumination correction on improving the performance of textile color quality detection. The experimental results show that the method proposed in this paper can effectively improve the textile image presentation. The average color difference between the corrected image and the standard image is calculated to be 2.9991 using CIEDE2000, which reduces the color difference by about 24.43% compared with the traditional kernel extreme learning machine.