  Visually restoring underwater scenes primarily involves mitigating
interference from underwater media. Existing methods ignore the inherent
scale-related characteristics in underwater scenes. Therefore, we present the
synergistic multi-scale detail refinement via intrinsic supervision (SMDR-IS)
for enhancing underwater scene details, which contain multi-stages. The
low-degradation stage from the original images furnishes the original stage
with multi-scale details, achieved through feature propagation using the
Adaptive Selective Intrinsic Supervised Feature (ASISF) module. By using
intrinsic supervision, the ASISF module can precisely control and guide feature
transmission across multi-degradation stages, enhancing multi-scale detail
refinement and minimizing the interference from irrelevant information in the
low-degradation stage. In multi-degradation encoder-decoder framework of
SMDR-IS, we introduce the Bifocal Intrinsic-Context Attention Module (BICA).
Based on the intrinsic supervision principles, BICA efficiently exploits
multi-scale scene information in images. BICA directs higher-resolution spaces
by tapping into the insights of lower-resolution ones, underscoring the pivotal
role of spatial contextual relationships in underwater image restoration.
Throughout training, the inclusion of a multi-degradation loss function can
enhance the network, allowing it to adeptly extract information across diverse
scales. When benchmarked against state-of-the-art methods, SMDR-IS consistently
showcases superior performance. The code is publicly available at:
https://github.com/zhoujingchun03/SMDR-IS.
