  In underwater image enhancement (UIE), convolutional neural networks (CNN)
have inherent limitations in modeling long-range dependencies and are less
effective in recovering global features. While Transformers excel at modeling
long-range dependencies, their quadratic computational complexity with
increasing image resolution presents significant efficiency challenges.
Additionally, most supervised learning methods lack effective physical model
constraint, which can lead to insufficient realism and overfitting in generated
images. To address these issues, we propose a physical model constraint-based
underwater image enhancement framework, Mamba-UIE. Specifically, we decompose
the input image into four components: underwater scene radiance, direct
transmission map, backscatter transmission map, and global background light.
These components are reassembled according to the revised underwater image
formation model, and the reconstruction consistency constraint is applied
between the reconstructed image and the original image, thereby achieving
effective physical constraint on the underwater image enhancement process. To
tackle the quadratic computational complexity of Transformers when handling
long sequences, we introduce the Mamba-UIE network based on linear complexity
state space models. By incorporating the Mamba in Convolution block, long-range
dependencies are modeled at both the channel and spatial levels, while the CNN
backbone is retained to recover local features and details. Extensive
experiments on three public datasets demonstrate that our proposed Mamba-UIE
outperforms existing state-of-the-art methods, achieving a PSNR of 27.13 and an
SSIM of 0.93 on the UIEB dataset. Our method is available at
https://github.com/zhangsong1213/Mamba-UIE.
