Color correction for scanner and printer using B-spline CMAC neural networks

Abstract

The process of eliminating the color errors from the gamut mismatch, resolution conversion, quantization and nonlinearity between scanner and printer is as an essential issue of color reproduction. This paper presents a new formation based on the generalized inverse plant control for the color error reduction process. In our formulation, the printer input and scanner output corresponds to the input and output of a system plant respectively. Obviously, if the printer input equals the scanner output, then there are no color errors involved in the entire system. In other words, the plant becomes an identity system. To achieve this goal, a plant generalized inverse should be identified and added to the original system. Since the system of a combination of both scanner and printer is highly nonlinear, CMAC-based neural networks, which have the capability to learn arbitrary nonlinearity, are applied to identify the plant generalized inverse. The CMAC network is a perceptron-like feedforward structure with associative memory properties. Moreover, it learns orders of magnitude more rapidly than typical implementations of back propagation in the feedforward neural networks. Tests verify the effectiveness of the proposed method.