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In order to solve the problems of large number of parameters and loss of image texture and color when real images are transformed into animation style images, this paper proposed a multi-channel cartoon generative adversarial networks (MC＿CartoonGAN). Firstly, it reconstructed the multi-channel image data set by using HSCNN + and genetic algorithm, and obtained more abundant image information. Secondly, it used DenseNet for feature reuse to reduce the memory consumption of parameters and alleviate the problem of gradient disappearance. Finally, it reconstructed the loss function by introducing multi-channel color to reduce the loss of the generated image while ensuring the integrity of the generated image content. Experimental results show that the proposed multi-channel cartoon generative adversarial network(MC＿ CartoonGAN) has better quality in converting real images into cartoon style images. [ABSTRACT FROM AUTHOR]

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