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| Underwater Image Generation Method Based on Contrastive Learning with Hard Negative Samples |
| LIU Zijian1, WANG Xingmei1,2, CHEN Weijing1, ZHANG Wansong1, ZHANG Tianzi1 |
1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001;
2. National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001 |
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Abstract Image generation is essential to acquire scarce underwater images, and it is typically reliant on paired data. Considering the limitation of practical access to such data distributions in marine environments, a contrastive learning-based generative adversarial network(CL-GAN) is introduced to overcome the constraints of bijection in image domain. However, the model struggles to learn complex content features from noisy images due to the low quality of negative samples resulting from random sampling. To address this issue, a hard negative sample contrastive learning-based feature level GAN(HCFGAN) for underwater image generation is proposed. To improve the quality of negative samples, a hard negative sampling module(HNS) is designed to mine feature similarity between samples. The hard negative samples close to the anchor sample are incorporated into contrastive loss for complex feature learning. To ensure the complexity and comprehensiveness of negative samples, a negative sample generation module(NSG) is constructed. The adversarial training of NSG and HNS ensures the validity of hard negative samples. To enhance feature extraction capability and training stability of the model for underwater fuzzy images, a contextual feature generator and a global feature discriminator are designed. Experiments show that the underwater images generated by HCFGAN exhibit good authenticity and richness with practical value in underwater image generation.
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Received: 26 August 2024
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| Fund:Stable Supporting Fund of Acoustic Science and Technology Laboratory(No.JCKYS2024604SSJS006), Fundamental Research Funds for the Central Universities(No.3072024XX0602) |
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Corresponding Authors:
ZHANG Wansong, Ph.D., associate professor. His research interests include simulation and modeling, and pattern recognition.
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| About author:: LIU Zijian, Ph.D. candidate. His research interests include image generation and con-trastive learning. WANG Xingmei, Ph.D., professor. Her research interests include artificial intelligence, image processing and computer vision. CHEN Weijing, Master, assistant engineer. His research interests include underwater image translation. ZHANG Tianzi, Master student. Her research interests include underwater image re-storation and image processing. |
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2024, Vol. 37 
