Title: A MEC-BP-Adaboost neural network-based color correction algorithm for color image acquisition equipments
Abstract: 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.
Publication Year: 2016
Publication Date: 2016-01-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 26
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