  Underwater Image Enhancement (UIE) is critical for marine research and
exploration but hindered by complex color distortions and severe blurring.
Recent deep learning-based methods have achieved remarkable results, yet these
methods struggle with high computational costs and insufficient global
modeling, resulting in locally under- or over- adjusted regions. We present
PixMamba, a novel architecture, designed to overcome these challenges by
leveraging State Space Models (SSMs) for efficient global dependency modeling.
Unlike convolutional neural networks (CNNs) with limited receptive fields and
transformer networks with high computational costs, PixMamba efficiently
captures global contextual information while maintaining computational
efficiency. Our dual-level strategy features the patch-level Efficient Mamba
Net (EMNet) for reconstructing enhanced image feature and the pixel-level
PixMamba Net (PixNet) to ensure fine-grained feature capturing and global
consistency of enhanced image that were previously difficult to obtain.
PixMamba achieves state-of-the-art performance across various underwater image
datasets and delivers visually superior results. Code is available at:
https://github.com/weitunglin/pixmamba.
