基於小腦運算模式之電子掃描及印刷色彩補正機構研究

Abstract

本論文提出一個整合彩色取像系統及印刷系統串接的色彩矯正機構。為達 成高傳真的色彩重現於複製影像, 必須要解決彩色取像器(Color scanner)及彩色印表機(Color printer)設備之間色彩表現的控制, 在此 我們提出一個逆向串接控制系統, 以期能同時消除色彩在輸入端與輸出端 所各別產生的彩色誤差即色彩不匹配的問題。一般而言,取像器對色彩的 響應隨著不同系統有不同的特性, 且其所用的加成性色座標系統與彩色印 表機的互補式色座標系統不同, 除此之外, 油墨色階的非線性、不同的套 色方法、紙張等等皆是色彩複製的誤差來源, 傳統的線性色彩矯正將無法 解決高度非線性的色彩對應, 因此為解決上述困難, 我們使用一種新的方 法, 即利用學者 Albus 所提小腦運算控制系統經由學習訓練逆向串接系 統, 使得輸入影像色彩能夠完整重現。另外、其快速的學習速度能在允許 時間內處理不同設備之間色彩不匹配的問題。 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. This thesis presents a new formulation based on the generalized inverse plant control for the color error reduction process. In our formulation, the printer input and scanner output correspond 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, a CMAC-neural networks, which have the capability to learn arbitrary nonlinearity, are applied to identify the plant generalized inverse. Moreover, its convergence rate is extremely high. Finally, a number of test samples are conducted to verify the effectiveness of the proposed method.