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：水下图像的降解是由于波长依赖光的衰减、散射以及所捕集的水类型的多样性造成的。深神经网络在这一领域迈出了一步,提供了能够增强水下图像的自主模型。我们根据VQGAN的离散特征量化模型,引入了水下胶囊矢量器GAN UWCVGAN。拟议的UWCVGAN将一个编码网络组合在一起,将图像压缩成其潜表层,并有一个解码网络,能够从唯一的潜表层中重建图像的增强。与VQGAN不同的是,UWCVGAN通过利用胶囊层的集集化能力实现特征的量化,使模型完全可以培训并易于管理。该模型获得了高质量的强化水下图像和精细细节。此外,经过培训的编码器独立于解码器,可以将图像嵌入收藏器作为压缩器,以缩小图像所需的记忆空间,用3\-时间参数进行要素对比,并用当前定量数据进行定量分析。