  Most deep models for underwater image enhancement resort to training on
synthetic datasets based on underwater image formation models. Although
promising performances have been achieved, they are still limited by two
problems: (1) existing underwater image synthesis models have an intrinsic
limitation, in which the homogeneous ambient light is usually randomly
generated and many important dependencies are ignored, and thus the synthesized
training data cannot adequately express characteristics of real underwater
environments; (2) most of deep models disregard lots of favorable underwater
priors and heavily rely on training data, which extensively limits their
application ranges. To address these limitations, a new underwater synthetic
dataset is first established, in which a revised ambient light synthesis
equation is embedded. The revised equation explicitly defines the complex
mathematical relationship among intensity values of the ambient light in RGB
channels and many dependencies such as surface-object depth, water types, etc,
which helps to better simulate real underwater scene appearances. Secondly, a
unified framework is proposed, named ANA-SYN, which can effectively enhance
underwater images under collaborations of priors (underwater domain knowledge)
and data information (underwater distortion distribution). The proposed
framework includes an analysis network and a synthesis network, one for priors
exploration and another for priors integration. To exploit more accurate
priors, the significance of each prior for the input image is explored in the
analysis network and an adaptive weighting module is designed to dynamically
recalibrate them. Meanwhile, a novel prior guidance module is introduced in the
synthesis network, which effectively aggregates the prior and data features and
thus provides better hybrid information to perform the more reasonable image
enhancement.
