Abstract
Ethnic minority costume culture is an indispensable part of ethnic minority culture and an important content of ethnic minority culture protection and inheritance. It plays a very important role in Chinese traditional culture. The coloring of minority costume sketches has many practical application environments. It is a research topic with scientific significance and application prospects. On the basis of coloring the sketches of ethnic minority costumes on the GAN network, this paper proposes a coloring model of ethnic clothing sketches based on the Pix2Pix network, which can automatically colorize ethnic clothing sketches. The network is implemented based on the CGAN network. Among them, the ResNet is used as the network Generator. In order to achieve the constraints on the target image generation process and further ensure the coloring effect of the generated image, we use the ethnic minority costume sketch as a “label” input in the Generator, and the L1 loss is used as the loss function. The network is trained on the data set constructed in this paper. In order to verify the effectiveness of the network, we compared it with a variety of coloring methods. The results show that the peak signal-to-noise ratio reaches 24.061 and the structural similarity reaches 0.820, which further verifies that the coloring method proposed in this paper has good coloring performance.
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Acknowledgement
This work is supported by National Natural Science Foundation of China (No. 61862068), Yunnan Expert Workstation of Xiaochun Cao, and Scientific Technology Innovation Team of Educational Big Data Application Technology in University of Yunnan Province.
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Wang, H., Zhou, J., Gan, J., Zou, W. (2021). Automatic Coloring Method for Ethnic Costume Sketch Based on Pix2Pix Network. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_33
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DOI: https://doi.org/10.1007/978-981-16-7476-1_33
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