  Underwater image analysis is crucial for marine monitoring. However, it
presents two major challenges (i) the visual quality of the images is often
degraded due to wavelength-dependent light attenuation, scattering, and water
types; (ii) capturing and storing high-resolution images is limited by
hardware, which hinders long-term environmental analyses. Recently, deep neural
networks have been introduced for underwater enhancement yet neglecting the
challenge posed by the limitations of autonomous underwater image acquisition
systems. We introduce a novel architecture that jointly tackles both issues by
drawing inspiration from the discrete features quantization approach of Vector
Quantized Variational Autoencoder (\myVQVAE). Our model combines an encoding
network, that compresses the input into a latent representation, with two
independent decoding networks, that enhance/reconstruct images using only the
latent representation. One decoder focuses on the spatial information while the
other captures information about the entities in the image by leveraging the
concept of capsules. With the usage of capsule layers, we also overcome the
differentiabilty issues of \myVQVAE making our solution trainable in an
end-to-end fashion without the need for particular optimization tricks.
Capsules perform feature quantization in a fully differentiable manner. We
conducted thorough quantitative and qualitative evaluations on 6 benchmark
datasets to assess the effectiveness of our contributions. Results demonstrate
that we perform better than existing methods (eg, about $+1.4dB$ gain on the
challenging LSUI Test-L400 dataset), while significantly reducing the amount of
space needed for data storage (ie, $3\times$ more efficient).
