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A Novel Combined GAN for Defects Generation Using Masking Mechanisms

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Pattern Recognition and Computer Vision (PRCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15034))

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Abstract

Defective images generated by generative adversarial networks (GANs) often exhibit insufficiently constructed defects. The discriminator’s dominance leads the generator to prioritize generating color blocks favored by the discriminator, disregarding the original information. In this paper, we propose a combined GAN that retains feature information, which comprises a defect generating GAN and a mask generating GAN. The two trained GANs are synergistically combined to generate defective images. Additionally, the structuration loss introduced in this paper guides and constrains the GAN model, aiming to preserve texture trends, grayscale distribution, and narrow defect regions. Experimental results show that our model produces high-quality images, with texture information closer to the original sample, and without the additional discriminators. This approach is evaluated against the latest detection model, demonstrating 4% improvement in effectiveness over the standard enhancement method.

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Authors and Affiliations

  1. South-Central Minzu University, Wuhan, China

    Zhiwei Bai, Bo Li, Xiaoqian Ma & Li Cheng

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  1. Zhiwei Bai
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  2. Bo Li
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  3. Xiaoqian Ma
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  4. Li Cheng
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Corresponding author

Correspondence to Bo Li .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Zhouchen Lin

  2. Nankai University, Tianjin, China

    Ming-Ming Cheng

  3. Chinese Academy of Sciences, Beijing, China

    Ran He

  4. Xinjiang University, Ürümqi, Xinjiang, China

    Kurban Ubul

  5. Xinjiang University, Ürümqi, China

    Wushouer Silamu

  6. Peking University, Beijing, China

    Hongbin Zha

  7. Tsinghua University, Beijing, China

    Jie Zhou

  8. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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Bai, Z., Li, B., Ma, X., Cheng, L. (2025). A Novel Combined GAN for Defects Generation Using Masking Mechanisms. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15034. Springer, Singapore. https://doi.org/10.1007/978-981-97-8505-6_7

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  • DOI: https://doi.org/10.1007/978-981-97-8505-6_7

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  • Print ISBN: 978-981-97-8504-9

  • Online ISBN: 978-981-97-8505-6

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