  Underwater image enhancement (UIE) is a meaningful but challenging task, and
many learning-based UIE methods have been proposed in recent years. Although
much progress has been made, these methods still exist two issues: (1) There
exists a significant region-wise quality difference in a single underwater
image due to the underwater imaging process, especially in regions with
different scene depths. However, existing methods neglect this internal
characteristic of underwater images, resulting in inferior performance; (2) Due
to the uniqueness of the acquisition approach, underwater image acquisition
tools usually capture multiple images in the same or similar scenes. Thus, the
underwater images to be enhanced in practical usage are highly correlated.
However, when processing a single image, existing methods do not consider the
rich external information provided by the related images. There is still room
for improvement in their performance. Motivated by these two aspects, we
propose a novel internal-external representation learning (UIERL) network to
better perform UIE tasks with internal and external information,
simultaneously. In the internal representation learning stage, a new
depth-based region feature guidance network is designed, including a region
segmentation based on scene depth to sense regions with different quality
levels, followed by a region-wise space encoder module. With performing
region-wise feature learning for regions with different quality separately, the
network provides an effective guidance for global features and thus guides
intra-image differentiated enhancement. In the external representation learning
stage, we first propose an external information extraction network to mine the
rich external information in the related images. Then, internal and external
features interact with each other via the proposed external-assist-internal
module and internal-assist-e
