Fast underwater image enhancement based on a generative adversarial framework

Guan, Yang; Liu, Xiaoyan; Yu, Zhibin; Wang, Yübo; Zheng, Xingyu; Zhang, Shaoda; Zheng, Bing

doi:10.3389/fmars.2022.964600

Cited by 10 publications

(3 citation statements)

References 65 publications

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“…The proposed UW-GAN exhibited superiority in single underwater image depth estimation and enhancement. Furthermore, other researchers [16] [17] proposed GAN-based adaptive underwater image enhancement methods, allowing dynamic adjustment of network structures or parameters during the training process to adapt to different input data or tasks, thereby achieving better performance in various scenarios or tasks.…”

Section: ) Gan-based Methodsmentioning

confidence: 99%

Residual Dense Blocks and Contrastive Regularization Integrated Underwater Image Enhancement Network

Zhao,

Yuan

2023

IEEE Access

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Due to the influence of optical properties and water quality conditions in underwater environments, underwater images often encounter challenges such as light attenuation, scattering, and absorption, resulting in low image quality, including blurriness, dimness, and lack of details. These limitations impose restrictions on the successful implementation of underwater vision tasks. Nonetheless, the significance of original underwater images in the context of feature extraction and learning tends to be disregarded by the majority of existing methodologies. This paper addresses this oversight by introducing techniques known as Residual Dense Blocks (RDB) and Contrastive Regularization (CR). These techniques serve to amplify the influence of the original underwater images, facilitating the construction of an end-toend fully convolutional network tailored for the purpose of enhancing underwater image quality. This innovative network is denoted as RDCR. By leveraging the local and global feature fusion of RDB and the contrastive learning of CR, this model effectively extracts multi-level features from the original images, adaptively preserving hierarchical features, and achieves high-quality underwater image enhancement through learning from the original images. The superiority of our proposed model over alternative comparative algorithms is evidenced by the experimental results obtained across four distinct datasets, encompassing assessments of both subjective visual perception and objective evaluation.INDEX TERMS Underwater image enhancement, deep learning methods, residual dense network.

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Section: ) Gan-based Methodsmentioning

confidence: 99%

Residual Dense Blocks and Contrastive Regularization Integrated Underwater Image Enhancement Network

Zhao,

Yuan

2023

IEEE Access

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“… Enhancement in Generative Adversarial Frameworks: The application of generative adversarial frameworks for rapid underwater image enhancement serves as a prime example of the technological innovation required. Such frameworks are essential in meeting the visual demands of underwater AR systems, indicating a pivotal direction for future development [68].…”

Section: Evaluation and Iterationmentioning

confidence: 99%

Integrating augmented reality, gesture recognition, and NLP for enhancing underwater human-robot interaction

Durga Deepak Valluri

2024

Int. J. Sci. Res. Arch.

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This paper presents an in-depth exploration into the integration of augmented reality (AR), gesture recognition, and natural language processing (NLP) to enhance human-robot interaction (HRI) within the context of underwater robotics. It highlights the significant potential these technologies hold in addressing the unique challenges faced in underwater environments, such as limited visibility, complex navigation, and the need for precise, intuitive communication between divers and robots. By reviewing current technological advancements and applications, the study underscores the critical role of AR in providing real-time visual feedback, gesture recognition in enabling more natural control mechanisms, and NLP in facilitating voice-driven commands and interactions. The research further discusses the development of a conceptual framework for an AR-based intuitive interface that synergizes gesture recognition and NLP, aiming to revolutionize underwater HRI by making it more efficient, safe, and user-friendly. Through this investigation, the paper seeks to contribute to the advancement of underwater robotics, proposing innovative solutions that could significantly improve human-robot collaboration in challenging aquatic missions.

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“…Lopez-Vazquez et al (2023) proposed a convolutional residual network for underwater image enhancement, while achieving high classification accuracy. Guan et al (2023) proposed a lightweight underwater image enhancement model based on GAN, which improves the quality and efficiency of underwater image generation. Meanwhile, many scholars have done extensive research on the object detection of marine organisms.…”

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confidence: 99%

Lightweight marine biological target detection algorithm based on YOLOv5

Liang

Song

2023

Front. Mar. Sci.

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Underwater object detection currently faces many challenges, such as the large number of parameters in existing object detection models, slow inference speed, blurring of underwater images, and aggregation of small targets, making it difficult to conduct efficient underwater object detection. This paper proposes a lightweight underwater object detection algorithm based on YOLOv5.The method uses depth-wise separable convolution instead of ordinary convolution to reduce the number of parameters and computational complexity. A C3 module based on Ghost convolution is designed to further compress the model size and improve the computational speed. In the feature extraction stage, a RepVgg module based on structural reparameterization is used to convert the multi -branch structure into a single-branch structure in the inference stage, improving the feature extraction ability of the model and increasing the inference speed. A Rep-ECA module is designed to embed the efficient channel attention module ECANet into the RepVGG module, selecting more effective channel information and improving the model’s feature extraction ability for small objects in blurred images, thereby improving detection precision. Experimental results show that in the URPC underwater object detection dataset, the proposed algorithm has a 39% lower model parameter count compared to the original model, a 42% reduction in computational complexity. The model can achieve a frame rate of 85 on a single Nvidia GTX 1080ti GPU, which is a 24% improvement over the original model, while mAP reaches 85.1%, a 1.1% improvement over the original model. The algorithm can improve the detection precision and achieve lightweight, which lays a foundation for the deployment of underwater equipment.

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Fast underwater image enhancement based on a generative adversarial framework

Cited by 10 publications

References 65 publications

Residual Dense Blocks and Contrastive Regularization Integrated Underwater Image Enhancement Network

Residual Dense Blocks and Contrastive Regularization Integrated Underwater Image Enhancement Network

Integrating augmented reality, gesture recognition, and NLP for enhancing underwater human-robot interaction

Lightweight marine biological target detection algorithm based on YOLOv5

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