Underwater image enhancement (UIE) face significant challenges due to complex underwater lighting conditions. Recently, mamba-based methods have achieved promising results in image enhancement tasks. However, these methods commonly rely on Vmamba, which focuses only on spatial information modeling and struggles to deal with the cross-color channel dependency problem in underwater images caused by the differential attenuation of light wavelengths, limiting the effective use of deep networks. In this paper, we propose a novel UIE framework called O-mamba. O-mamba employs an O-shaped dual-branch network to separately model spatial and cross-channel information, utilizing the efficient global receptive field of state-space models optimized for underwater images. To enhance information interaction between the two branches and effectively utilize multi-scale information, we design a Multi-scale Bi-mutual Promotion Module. This branch includes MS-MoE for fusing multi-scale information within branches, Mutual Promotion module for interaction between spatial and channel information across branches, and Cyclic Multi-scale optimization strategy to maximize the use of multi-scale information. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) results.The code is available at https://github.com/chenydong/O-Mamba.

翻译：水下图像增强（UIE）由于复杂的水下光照条件面临重大挑战。近期，基于Mamba的方法在图像增强任务中取得了令人瞩目的成果。然而，这些方法通常依赖于Vmamba，其仅关注空间信息建模，难以处理由光波长差异衰减引起的水下图像跨颜色通道依赖问题，限制了深度网络的有效利用。本文提出了一种名为O-Mamba的新型UIE框架。O-Mamba采用O形双分支网络分别建模空间信息和跨通道信息，利用了针对水下图像优化的状态空间模型所具备的高效全局感受野。为了增强两个分支间的信息交互并有效利用多尺度信息，我们设计了一个多尺度双向互促模块。该模块包含用于融合分支内多尺度信息的MS-MoE、用于跨分支空间与通道信息交互的互促模块，以及旨在最大化利用多尺度信息的循环多尺度优化策略。大量实验表明，我们的方法取得了最先进的（SOTA）成果。代码可在 https://github.com/chenydong/O-Mamba 获取。