Through discussing the color matching technology and its application in printing industry the conventional approaches commonly used in color matching, and the difficulties in color matching, a nonlinear color matching...Through discussing the color matching technology and its application in printing industry the conventional approaches commonly used in color matching, and the difficulties in color matching, a nonlinear color matching model based on two step learning is established by finding a linear model by learning pure color data first and then a nonlinear modification model by learning mixed color data. Nonlinear multiple regression is used to fit the parameters of the modification model. Nonlinear modification function is discovered by BACON system by learning mixture data. Experiment results indicate that nonlinear color conversion by two step learning can further improve the accuracy when it is used for straightforward conversion from RGB to CMYK. An improved separation model based on GCR concept is proposed to solve the problem of gray balance and it can be used for three to four color conversion as well. The method proposed has better learning ability and faster printing speed than other historical approaches when it is applied to four color ink jet printing.展开更多
This paper proposes a CNN-Transformer hybrid model for ink formulation prediction,named CTNet.The model leverages Convolutional Neural Networks(CNN)to extract local features from the spectral reflectance of sample sur...This paper proposes a CNN-Transformer hybrid model for ink formulation prediction,named CTNet.The model leverages Convolutional Neural Networks(CNN)to extract local features from the spectral reflectance of sample surfaces and incorporates the self-attention mechanism of the Transformer to achieve efficient mapping between color and formulation.In addition,Bayesian optimization is introduced for hyperparameter tuning,further enhancing model performance.Experimental results demonstrate that CTNet outperforms CNN,RNN,LSTM,and the standard Transformer model in terms of Mean Absolute Error(MAE),achieving higher prediction accuracy.This provides an effective solution for high-precision and automated ink color matching,showing promising potential for industrial applications.展开更多
文摘Through discussing the color matching technology and its application in printing industry the conventional approaches commonly used in color matching, and the difficulties in color matching, a nonlinear color matching model based on two step learning is established by finding a linear model by learning pure color data first and then a nonlinear modification model by learning mixed color data. Nonlinear multiple regression is used to fit the parameters of the modification model. Nonlinear modification function is discovered by BACON system by learning mixture data. Experiment results indicate that nonlinear color conversion by two step learning can further improve the accuracy when it is used for straightforward conversion from RGB to CMYK. An improved separation model based on GCR concept is proposed to solve the problem of gray balance and it can be used for three to four color conversion as well. The method proposed has better learning ability and faster printing speed than other historical approaches when it is applied to four color ink jet printing.
文摘This paper proposes a CNN-Transformer hybrid model for ink formulation prediction,named CTNet.The model leverages Convolutional Neural Networks(CNN)to extract local features from the spectral reflectance of sample surfaces and incorporates the self-attention mechanism of the Transformer to achieve efficient mapping between color and formulation.In addition,Bayesian optimization is introduced for hyperparameter tuning,further enhancing model performance.Experimental results demonstrate that CTNet outperforms CNN,RNN,LSTM,and the standard Transformer model in terms of Mean Absolute Error(MAE),achieving higher prediction accuracy.This provides an effective solution for high-precision and automated ink color matching,showing promising potential for industrial applications.
