Abstract
Generative adversarial networks have yielded outstanding results in unsupervised areas of learning, but existing research has proven that the results are not stable in specific areas. In this paper, an improved generative adversarial networks model is proposed. First, the loss calculation method of the generated model is changed, which makes the removal target of the whole network controllable. Second, the deep convolution network is added to the existing network; this improves the accuracy of the mosaic removal. And then combines the loss calculation method of the pixel networks, the network effectively solve the unstable features of generative adversarial networks in specific conditions. Finally, the experimental results show that the overall mosaic face removal for this network performance is superior to other existing algorithms.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (No. U1536121, 61370195).
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Wang, H., Cao, Z., Niu, S., Tong, H. (2020). Mosaic Removal Algorithm Based on Improved Generative Adversarial Networks Model. In: Pan, JS., Li, J., Tsai, PW., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 157. Springer, Singapore. https://doi.org/10.1007/978-981-13-9710-3_37
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DOI: https://doi.org/10.1007/978-981-13-9710-3_37
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