Underwater environments pose significant challenges for image capture due to factors like light absorption, scattering, and the presence of particles in the water. These factors degrade the quality of underwater images, impacting tasks like target detection and recognition. The challenge with deep learning-based underwater image enhancement methods is their reliance on paired datasets, which consist of degraded and corresponding ground-truth images. Obtaining such paired datasets in natural conditions is challenging, leading to performance issues in these algorithms. To address this issue, we propose an unsupervised generative adversarial network with edge detection for enhancing underwater images without needing paired data. First, we introduce the perceptual loss function into the conventional loss function to better measure the performance of two generative networks. Second, we propose an edge extraction block based on the Laplacian operator, an attention module with an edge extraction block, a multi-scale feature module, a novel upsampling module, and a new downsampling module. We use these proposed modules to design a new generative network. Third, we use the proposed multi-scale feature and downsampling modules to design the adversarial network. We tested the algorithm’s performance on both synthetic and authentic underwater images. Compared to existing state-of-the-art methods, our proposed approach better enhances image details and restores color information.

中文翻译：

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一种基于生成对抗网络的边缘提取无监督水下图像增强方法


由于光吸收、散射和水中颗粒的存在等因素，水下环境对图像捕获提出了重大挑战。这些因素会降低水下图像质量，从而影响目标检测和识别等任务。基于深度学习的水下图像增强方法的挑战在于它们对配对数据集的依赖，这些数据集由降级和相应的真实图像组成。在自然条件下获取此类配对数据集具有挑战性，从而导致这些算法出现性能问题。为了解决这个问题，我们提出了一种具有边缘检测功能的无监督生成对抗网络，用于增强水下图像，而无需配对数据。首先，我们将感知损失函数引入传统的损失函数中，以更好地测量两个生成网络的性能。其次，我们提出了一种基于 Laplacian 算子的边缘提取模块、带有边缘提取块的注意力模块、多尺度特征模块、新颖的上采样模块和新的下采样模块。我们使用这些建议的模块来设计一个新的生成网络。第三，我们使用提出的多尺度特征和降采样模块来设计对抗网络。我们测试了该算法在合成和真实水下图像上的性能。与现有的最先进的方法相比，我们提出的方法更好地增强了图像细节并恢复了颜色信息。