  In underwater environments, variations in suspended particle concentration
and turbidity cause severe image degradation, posing significant challenges to
image enhancement (IE) and object detection (OD) tasks. Currently, in-air image
enhancement and detection methods have made notable progress, but their
application in underwater conditions is limited due to the complexity and
variability of these environments. Fine-tuning in-air models saves high
overhead and has more optional reference work than building an underwater model
from scratch. To address these issues, we design a transfer plugin with
multiple priors for converting in-air models to underwater applications, named
IA2U. IA2U enables efficient application in underwater scenarios, thereby
improving performance in Underwater IE and OD. IA2U integrates three types of
underwater priors: the water type prior that characterizes the degree of image
degradation, such as color and visibility; the degradation prior, focusing on
differences in details and textures; and the sample prior, considering the
environmental conditions at the time of capture and the characteristics of the
photographed object. Utilizing a Transformer-like structure, IA2U employs these
priors as query conditions and a joint task loss function to achieve
hierarchical enhancement of task-level underwater image features, therefore
considering the requirements of two different tasks, IE and OD. Experimental
results show that IA2U combined with an in-air model can achieve superior
performance in underwater image enhancement and object detection tasks. The
code will be made publicly available.
