Neural plant inverse control approach to color error reduction for scanner and printer

The process of eliminating the color errors from the gamut mismatch, resolution conversion, quantization and nonlinearity between scanner and printer is usually recognized as an essential issue of color reproduction. A new formulation based on the inverse plant control for the color error reduction process is presented. In this formulation, the printer input and scanner output correspond to the input and output of a system plant respectively. If the printer input equals the scanner output, there are no color errors involved in the entire system, i.e., the plant becomes an identity system. To achieve this goal, a plant inverse should be identified and added to the original system. Since the system of a combination of both scanner and printer is highly nonlinear, multilayer backpropagation neural networks, which have the capability to learn arbitrary nonlinearity, are applied to identify the plant inverse. A number of test samples are conducted to verify the effectiveness of the proposed method