Modern chromatic equipment has been widely used. With the popularity of color digital equipment, accurate acquisition of color has a wide range of uses. The colorimetric characterization of equipment is the basic link of color management system, and how to accurately convert the color of various color devices has become a basic problem. The color space of the camera depends on the equipment. Therefore, the colorimetric characterization of digital camera is an important method to improve the color reproduction of images, which is the basis of color conversion between various devices. In the process of camera colorimetric characterization, the traditional neural network method, such as Back propagation(BP) neural network, needs a large number of sample data to obtain a large number of data, and the processing is complex. Because of its fast training speed, small amount of data and small color difference, RBF neural network can be used to solve the problem of colorimetric characterization of digital camera. On the 140 color blocks, half is used as training data set and half as test data set. In the first part, RBF neural network is used to train the data set, and the second part is used to experiment with the traditional BP neural network under the same data set. The experimental results show that the average color difference of training samples is 1.79ΔΕ CMC(1:1), the average color difference of the test sample is 4.89 ΔΕ. Compared with the traditional polynomial fitting method and BP network, RBF neural network has smaller color difference in colorimetric characterization.
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