The degradation in the underwater images is due to wavelength-dependent light attenuation, scattering, and to the diversity of the water types in which they are captured. Deep neural networks take a step in this field, providing autonomous models able to achieve the enhancement of underwater images. We introduce Underwater Capsules Vectors GAN UWCVGAN based on the discrete features quantization paradigm from VQGAN for this task. The proposed UWCVGAN combines an encoding network, which compresses the image into its latent representation, with a decoding network, able to reconstruct the enhancement of the image from the only latent representation. In contrast with VQGAN, UWCVGAN achieves feature quantization by exploiting the clusterization ability of capsule layer, making the model completely trainable and easier to manage. The model obtains enhanced underwater images with high quality and fine details. Moreover, the trained encoder is independent of the decoder giving the possibility to be embedded onto the collector as compressing algorithm to reduce the memory space required for the images, of factor $3\times$. \myUWCVGAN{ }is validated with quantitative and qualitative analysis on benchmark datasets, and we present metrics results compared with the state of the art.

翻译：水下图像的退化归因于波长依赖的光衰减、散射以及拍摄时所处水域类型的多样性。深度神经网络在这一领域迈出了重要一步，提供了能够实现水下图像增强的自主模型。我们基于VQGAN的离散特征量化范式，提出了水下胶囊向量生成对抗网络（UW-CVGAN）以解决此任务。所提出的UW-CVGAN结合了一个将图像压缩为潜在表示的编码网络，以及一个能够仅从潜在表示重建增强后图像的解码网络。与VQGAN不同，UW-CVGAN通过利用胶囊层的聚类能力实现特征量化，使模型完全可训练且更易于管理。该模型可获得高质量且细节丰富的水下增强图像。此外，训练好的编码器独立于解码器，可作为压缩算法嵌入采集设备，将图像所需存储空间缩减至原来的$3\times$。我们通过在基准数据集上的定量和定性分析验证了UW-CVGAN，并呈现了与当前最优方法对比的指标结果。