IA2U: A Transfer Plugin with Multi-Prior for In-Air Model to Underwater
- URL: http://arxiv.org/abs/2312.06955v2
- Date: Fri, 19 Jan 2024 01:47:22 GMT
- Title: IA2U: A Transfer Plugin with Multi-Prior for In-Air Model to Underwater
- Authors: Jingchun Zhou and Qilin Gai and Kin-man Lam and Xianping Fu
- Abstract summary: In underwater environments, variations in suspended particle concentration and turbidity cause severe image degradation.
We develop a transfer plugin with multiple priors for converting in-air models to underwater applications, named IA2U.
We show that IA2U combined with an in-air model can achieve superior performance in underwater image enhancement and object detection tasks.
- Score: 18.491734287988304
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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