Image data enhancement method based on conditional generative adversarial network and Poisson blending

doi:10.3785/j.issn.1008-973X.2026.12.005

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (12): 2592-2601 DOI: 10.3785/j.issn.1008-973X.2026.12.005

Image data enhancement method based on conditional generative adversarial network and Poisson blending

Kejia CHEN(

),Weiwen ZENG,Zhenqing WANG

School of Economics and Management, Fuzhou University, Fuzhou 350108, China

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Abstract

An image data augmentation method based on conditional generative adversarial learning and Poisson blending was proposed in order to address the long-tailed distribution and label scarcity in image datasets. A Wasserstein conditional generative adversarial network with gradient penalty was constructed by incorporating class-conditional information in order to enable class-controllable image generation. Poisson blending was employed to improve the smoothness and visual coherence of the generated images. Experiments conducted on CIFAR-10 and the Huawei-Garbage dataset demonstrated that the proposed method outperformed representative models such as StyleGAN, ACGAN and WGAN-Div in terms of generation quality and training stability. The proposed method approached or exceeded the performance of denoising diffusion probabilistic models with a shorter generation time. Ablation studies verified the effectiveness of each constituent module. The proposed method significantly improves the structural integrity and semantic consistency of generated images, effectively alleviates the data imbalance problem, and demonstrates strong practical applicability.

Key words： image data augmentation long-tailed distribution label scarcity conditional generative adversarial network Poisson blending

Received: 14 September 2025 Published: 21 October 2026

CLC:

TP 391

Fund: 国家自然科学基金资助项目(72374030)；福建省财政厅专项资金资助项目(0300-83022110).

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Cite this article:

Kejia CHEN,Weiwen ZENG,Zhenqing WANG. Image data enhancement method based on conditional generative adversarial network and Poisson blending. Journal of ZheJiang University (Engineering Science), 2026, 60(12): 2592-2601.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.12.005 OR https://www.zjujournals.com/eng/Y2026/V60/I12/2592

基于条件生成对抗和泊松融合的图像数据增强方法

为了解决图像数据集中长尾分布与标签稀缺问题，提出基于条件生成对抗学习和泊松融合的图像数据增强方法. 该方法通过引入条件信息构建具有梯度惩罚的Wasserstein条件生成对抗网络以实现类别可控的图像生成，采用泊松融合提升生成图像的平滑度与视觉连贯性. 在CIFAR-10和“华为云”垃圾分类数据集上的实验表明，该方法在生成质量和训练稳定性上优于StyleGAN、ACGAN、WGAN-Div等主流模型，且以较短的生成时间接近或超越去噪扩散概率模型的性能，通过消融实验验证了各模块的有效性. 该方法显著提升了生成图像的结构完整性与语义一致性，有效解决了数据不平衡问题，具有良好的实用价值.

关键词： 图像数据增强, 长尾分布, 标签稀缺, 条件生成对抗网络, 泊松融合

Fig.1 Structure of PB-WCGAN-GP network

Fig.2 Structure of PB-WCGAN-GP generator

Fig.3 Architecture of convolutional block attention module

Fig.4 Structure of PB-WGAN-GP critic

Fig.5 Principle of WGAN convergence determination

Fig.6 Structure of Poisson -blending integrated diagnosing module

Tab.1 Comparison of parameters and FLOPs for different generative networks

Tab.2 Comparison of IS and FID for different generative networks

Tab.3 Ablation experiment results on different datasets

Tab.4 Comparison before and after data augmentation

Fig.7 Comparison of generated images on CIFAR-10

Fig.8 Comparison of generated images on Huawei-Garbage

Fig.9 PB-WCGAN-GP task-specific image generation on CIFAR-10-cat subset

Fig.10 Task-specific image generation comparison on Huawei-Garbage-20 subset (‘Recyclable/express paper bag’)


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