  Underwater images suffer from color casts and low contrast due to wavelength-
and distance-dependent attenuation and scattering. To solve these two
degradation issues, we present an underwater image enhancement network via
medium transmission-guided multi-color space embedding, called Ucolor.
Concretely, we first propose a multi-color space encoder network, which
enriches the diversity of feature representations by incorporating the
characteristics of different color spaces into a unified structure. Coupled
with an attention mechanism, the most discriminative features extracted from
multiple color spaces are adaptively integrated and highlighted. Inspired by
underwater imaging physical models, we design a medium transmission (indicating
the percentage of the scene radiance reaching the camera)-guided decoder
network to enhance the response of the network towards quality-degraded
regions. As a result, our network can effectively improve the visual quality of
underwater images by exploiting multiple color spaces embedding and the
advantages of both physical model-based and learning-based methods. Extensive
experiments demonstrate that our Ucolor achieves superior performance against
state-of-the-art methods in terms of both visual quality and quantitative
metrics.
