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Algorithms 2018, 11(1), 4; https://doi.org/10.3390/a11010004

Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network

1,2
,
2
,
2,* , 1,2
and
1,2
1
Department of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
2
The Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Received: 30 October 2017 / Revised: 7 January 2018 / Accepted: 8 January 2018 / Published: 11 January 2018
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Abstract

Recently, inspired by the power of deep learning, convolution neural networks can produce fantastic images at the pixel level. However, a significant limiting factor for previous approaches is that they focus on some simple datasets such as faces and bedrooms. In this paper, we propose a multiscale deep neural network to transform sketches into Chinese paintings. To synthesize more realistic imagery, we train the generative network by using both L1 loss and adversarial loss. Additionally, users can control the process of the synthesis since the generative network is feed-forward. This network can also be treated as neural style transfer by adding an edge detector. Furthermore, additional experiments on image colorization and image super-resolution demonstrate the universality of our proposed approach. View Full-Text
Keywords: deep neural network; sketch; arts synthesis; style transfer deep neural network; sketch; arts synthesis; style transfer
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Lin, D.; Wang, Y.; Xu, G.; Li, J.; Fu, K. Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network. Algorithms 2018, 11, 4.

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