A MEC-BP-Adaboost neural network-based color correction algorithm for color image acquisition equipments

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• 作者：Jing Zhang^a ; YingPing Yang^a ; ^b ; ^{ypyang@whut.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; JinMin Zhang^a
• 关键词：MEC-BP-Adaboost neural network ; Color correction ; Polynomial regression ; Color space transformation
• 刊名：Optik - International Journal for Light and Electron Optics
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：127
• 期：2
• 页码：776-780
• 全文大小：499 K

文摘

Color correction algorithm based on color space conversion is an essential part in color management system. Subject to the variation in conditions such as the limitations on imaging-forming principle, device performances and machining controls, the captured images usually contain certain color distortion compared to the actual images. Therefore, the paper proposes a mind evolutionary computation (MEC)-back propagation (BP)-adaboost algorithm (Adaboost) neural network-based color correction algorithm for color image collecting equipment. MEC-BP-Adaboost is utilized in training process to establish the color mapping model, with the captured samples of the color-targets under the color image acquisition equipments as input data and the standard color data as output data. MEC-BP-Adaboost neural network-based color correction algorithm not only can further the correction accuracy, but also can solve the problem that the color correction models obtained by using the neural network methods are not unique, and the difference among that color correction models is very big. Experimental results demonstrate that the proposed method achieves much better correction performance than the polynomial regression model, the conventional BP neural network, and the genetic algorithm (GA)-BP neural network.