Cascade-CMAC neural network applications on the color scanner to printer calibration

This paper presents an application of using a cascade-CMAC (cerebellar model articulation controller) neural network to solve some color calibration problems, which include color differences induced from gamuts mis-match and the nonlinear transformation characteristics between color scanning input devices and color printing output devices. For this purpose, we proposed a scalable learning architecture “cascade-CMAC” to implement an adaptive color calibration system. By analyzing the preliminary learning situation, the scalable architecture can dynamically create a new learning unit to better represent a finer color resolution, so that the learning capacity as well as the color details of the system can be greatly improved. From the experimental results, the proposed cascade-CMAC architecture can improve the rate of convergence and also can adjust the learning architecture effectively. The learning speed can be 2~4 times faster than the conventional CMAC. The effectiveness of this neural network has been tested by observing the differences between the calibrated and the un-calibrated output on a number of known samples. By using the Macbeth color-checker which contains 24 color patches as benchmark, the average color differences between the original and the calibrated print-out is improved from 15 ΔE_ab to 8 ΔE_ab under the 3 ΔE_ab convergent criterion for training. The calibration performance is somewhat significant