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

<p>This paper proposes a digital camouflage generation method based on an improved CycleGAN to produce camouflage patterns with a high degree of fusion with the background and realistic texture details. Firstly, a SE-ResNet network structure is constructed by combining the residual network ResNet with the channel attention mechanism SENet, enabling flexible adjustment of channel weights to effectively extract crucial channel features and enhance the network's perception capability of important information in images. Secondly, a color preservation loss is introduced to improve the adversarial loss function, thereby avoiding training instability and fluctuation in pattern quality. Experimental results demonstrate that the camouflage patterns generated using the proposed method achieve a Structural Similarity Index (SSIM) of 0.77 and a Peak Signal-to-Noise Ratio (PSNR) of 18.9, representing improvements of 0.27 and 3.3, respectively, compared to the original CycleGAN. This method can generate digital camouflage patterns with richer details, textures, and high fusion with the background.</p>
</abstract>

This paper proposes a digital camouflage generation method based on an improved CycleGAN to produce camouflage patterns with a high degree of fusion with the background and realistic texture details. Firstly, a SE-ResNet network structure is constructed by combining the residual network ResNet with the channel attention mechanism SENet, enabling flexible adjustment of channel weights to effectively extract crucial channel features and enhance the network's perception capability of important information in images. Secondly, a color preservation loss is introduced to improve the adversarial loss function, thereby avoiding training instability and fluctuation in pattern quality. Experimental results demonstrate that the camouflage patterns generated using the proposed method achieve a Structural Similarity Index (SSIM) of 0.77 and a Peak Signal-to-Noise Ratio (PSNR) of 18.9, representing improvements of 0.27 and 3.3, respectively, compared to the original CycleGAN. This method can generate digital camouflage patterns with richer details, textures, and high fusion with the background.

Digital Camouflage Generation Based on an Improved CycleGAN Network Model

School of Computer Science and Engineering

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":"Digital Camouflage Generation Based on an Improved CycleGAN Network Model"}

This paper proposes a digital camouflage generation method based on an improved CycleGAN to produce camouflage patterns with a high degree of fusion with the...

Generative model	SSIM	PSNR
GAN	0.19	13.5
DRIT	0.48	14.8
CycleGAN+SENet+improved loss function	0.77	18.9

Model	SSIM	PSNR
CycleGAN	0.50	15.6

CycleGAN+SENet	0.61	16.7
CycleGAN+improved loss function	0.68	15.9
CycleGAN+SENet+improved loss function.	0.77	18.9

Digital Camouflage Generation Based on an Improved CycleGAN Network Model

Online veröffentlicht: 21. Juli 2024

Seitenbereich: 89 - 99

DOI: https://doi.org/10.2478/ijanmc-2024-0019

Schlüsselwörter
Digital Camouflage, CycleGAN, Channel Attention Mechanism, Residual Network

© 2024 Leixiang Xia 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.

Figure 6.

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Figure 8.

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Figure 10.

Comparative experimental evaluation score table

Evaluation index table of ablation experiments

Digital Camouflage Generation Based on an Improved CycleGAN Network Model

Online veröffentlicht: 21. Juli 2024

Seitenbereich: 89 - 99

DOI: https://doi.org/10.2478/ijanmc-2024-0019

SchlüsselwörterDigital Camouflage, CycleGAN, Channel Attention Mechanism, Residual Network

© 2024 Leixiang Xia 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.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Comparative experimental evaluation score table

Evaluation index table of ablation experiments

Schlüsselwörter
Digital Camouflage, CycleGAN, Channel Attention Mechanism, Residual Network