  Underwater image enhancement, as a pre-processing step to improve the
accuracy of the following object detection task, has drawn considerable
attention in the field of underwater navigation and ocean exploration. However,
most of the existing underwater image enhancement strategies tend to consider
enhancement and detection as two independent modules with no interaction, and
the practice of separate optimization does not always help the underwater
object detection task. In this paper, we propose two perceptual enhancement
models, each of which uses a deep enhancement model with a detection perceptor.
The detection perceptor provides coherent information in the form of gradients
to the enhancement model, guiding the enhancement model to generate patch level
visually pleasing images or detection favourable images. In addition, due to
the lack of training data, a hybrid underwater image synthesis model, which
fuses physical priors and data-driven cues, is proposed to synthesize training
data and generalise our enhancement model for real-world underwater images.
Experimental results show the superiority of our proposed method over several
state-of-the-art methods on both real-world and synthetic underwater datasets.
