<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Sea objects training, the conditional adversarial networks require a large number of images to solve image-to-image translation problems. In the case of insufficient samples, it leads to network overfitting and poor training results. This project proposes a conditional adversarial generative model that retains the original background features in the absence of paired samples. The goal of this project is to reduce the deviation of the corresponding output from the original input. Firstly, the object images of different categories are labeled with color masks. Second, sea objects are generated randomly in the original background using model of this project. Finally, the generated results of this approach are compared with other approaches. The experimental results show that, compared with results from other conditional adversarial generative models, the generated object images using model of this project have the characteristics of richer texture and clearer structure.</p>
</abstract>

Sea objects training, the conditional adversarial networks require a large number of images to solve image-to-image translation problems. In the case of insufficient samples, it leads to network overfitting and poor training results. This project proposes a conditional adversarial generative model that retains the original background features in the absence of paired samples. The goal of this project is to reduce the deviation of the corresponding output from the original input. Firstly, the object images of different categories are labeled with color masks. Second, sea objects are generated randomly in the original background using model of this project. Finally, the generated results of this approach are compared with other approaches. The experimental results show that, compared with results from other conditional adversarial generative models, the generated object images using model of this project have the characteristics of richer texture and clearer structure.

Generating Sea Surface Object Image Using Image-to-Image Translation

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Generating Sea Surface Object Image Using Image-to-Image Translation"}

Sea objects training, the conditional adversarial networks require a large number of images to solve image-to-image translation problems. In the case of...

Method	PSNR	SSIM
project model	18.453	90.39%
Pix2pixGAN model	16.874	80.42%
Bicycle-GAN model	18.096	87.61%
Pix2pix-HD model	17.919	86.42%

Generating Sea Surface Object Image Using Image-to-Image Translation

Publié en ligne: 21 févr. 2022

Pages: 48 - 55

DOI: https://doi.org/10.21307/ijanmc-2021-016

Mots clés
Image Generation, Conditional Generative Adversarial Network, Sea Surface Object, Image-To-Image Translation.

© 2021 Wenbin Yin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

COMPARISON OF EVALUATION INDEX OF DIFFERENT METHODS

EXPERIMENTAL ENVIRONMENT

Generating Sea Surface Object Image Using Image-to-Image Translation

Publié en ligne: 21 févr. 2022

Pages: 48 - 55

DOI: https://doi.org/10.21307/ijanmc-2021-016

Mots clésImage Generation, Conditional Generative Adversarial Network, Sea Surface Object, Image-To-Image Translation.

© 2021 Wenbin Yin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

COMPARISON OF EVALUATION INDEX OF DIFFERENT METHODS

EXPERIMENTAL ENVIRONMENT

Mots clés
Image Generation, Conditional Generative Adversarial Network, Sea Surface Object, Image-To-Image Translation.