  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.
