Underwater Image Enhancement (UIE) techniques aim to address the problem of
underwater image degradation due to light absorption and scattering. In recent
years, both Convolution Neural Network (CNN)-based and Transformer-based
methods have been widely explored. In addition, combining CNN and Transformer
can effectively combine global and local information for enhancement. However,
this approach is still affected by the secondary complexity of the Transformer
and cannot maximize the performance. Recently, the state-space model (SSM)
based architecture Mamba has been proposed, which excels in modeling long
distances while maintaining linear complexity. This paper explores the
potential of this SSM-based model for UIE from both efficiency and
effectiveness perspectives. However, the performance of directly applying Mamba
is poor because local fine-grained features, which are crucial for image
enhancement, cannot be fully utilized. Specifically, we customize the MambaUIE
architecture for efficient UIE. Specifically, we introduce visual state space
(VSS) blocks to capture global contextual information at the macro level while
mining local information at the micro level. Also, for these two kinds of
information, we propose a Dynamic Interaction Block (DIB) and Spatial
feed-forward Network (SGFN) for intra-block feature aggregation. MambaUIE is
able to efficiently synthesize global and local information and maintains a
very small number of parameters with high accuracy. Experiments on UIEB
datasets show that our method reduces GFLOPs by 67.4% (2.715G) relative to the
SOTA method. To the best of our knowledge, this is the first UIE model
constructed based on SSM that breaks the limitation of FLOPs on accuracy in
UIE. The official repository of MambaUIE at
this https URL

通过使用基于状态空间模型的 MambaUIE 方法，可以高效地合成全局和局部信息，仅具有少量参数，并提高准确性。