Texture synthesis method based on generative adversarial networks

Yu Siquan; Han Zhi; Tang Yandong; Wu Chengdong

doi:10.3788/IRLA201847.0203005

Texture synthesis is a hot research topic in the fields of computer graphics, vision, and image processing. Traditional texture synthesis methods are generally achieved by extracting effective feature patterns or statistics and generating random images under the constraint of the feature information. Generative adversarial networks (GANs) is a new type of deep network. It can randomly generate new data of the same distribution as the observed data by training generator and discriminator in an adversarial learning mechanism. Inspired by this point, a texture synthesis method based on GANs was proposed. The advantage of the algorithm was that it could generate more realistic texture images without iteration; the generated images were visually consistent with the observed texture image and also had randomness. A series of experiments for random texture and structured texture synthesis verify the effectiveness of the proposed algorithm.

Texture synthesis method based on generative adversarial networks

1. School of Information Science and Engineering,Northeastern University,Shenyang 110000,China;

2. State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110000,China

Received Date: 2017-09-05

Rev Recd Date: 2017-10-05

Publish Date: 2018-02-25

Key words:

Abstract: Texture synthesis is a hot research topic in the fields of computer graphics, vision, and image processing. Traditional texture synthesis methods are generally achieved by extracting effective feature patterns or statistics and generating random images under the constraint of the feature information. Generative adversarial networks (GANs) is a new type of deep network. It can randomly generate new data of the same distribution as the observed data by training generator and discriminator in an adversarial learning mechanism. Inspired by this point, a texture synthesis method based on GANs was proposed. The advantage of the algorithm was that it could generate more realistic texture images without iteration; the generated images were visually consistent with the observed texture image and also had randomness. A series of experiments for random texture and structured texture synthesis verify the effectiveness of the proposed algorithm.

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Reference (15)

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Texture synthesis method based on generative adversarial networks

doi: 10.3788/IRLA201847.0203005

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