Underwater images often suffer from severe degradation, such as color distortion, low contrast, and blurred details, due to light absorption and scattering in water. While learning-based methods like CNNs and Transformers have shown promise, they face critical limitations: CNNs struggle to model the long-range dependencies needed for non-uniform degradation, and Transformers incur quadratic computational complexity, making them inefficient for high-resolution images. To address these challenges, we propose Hero-Mamba, a novel Mamba-based network that achieves efficient dual-domain learning for underwater image enhancement. Our approach uniquely processes information from both the spatial domain (RGB image) and the spectral domain (FFT components) in parallel. This dual-domain input allows the network to decouple degradation factors, separating color/brightness information from texture/noise. The core of our network utilizes Mamba-based SS2D blocks to capture global receptive fields and long-range dependencies with linear complexity, overcoming the limitations of both CNNs and Transformers. Furthermore, we introduce a ColorFusion block, guided by a background light prior, to restore color information with high fidelity. Extensive experiments on the LSUI and UIEB benchmark datasets demonstrate that Hero-Mamba outperforms state-of-the-art methods. Notably, our model achieves a PSNR of 25.802 and an SSIM of 0.913 on LSUI, validating its superior performance and generalization capabilities.

翻译：水下图像常因水体对光的吸收和散射而出现严重退化，如颜色失真、对比度低和细节模糊。虽然基于CNN和Transformer等学习方法已展现出潜力，但存在关键局限：CNN难以建模非均匀退化所需的长距离依赖关系，而Transformer具有二次计算复杂度，处理高分辨率图像效率低下。为解决这些问题，我们提出Hero-Mamba——一种基于Mamba的新型网络，实现高效的双域学习以增强水下图像。本方法创新性地并行处理空间域（RGB图像）和频谱域（FFT分量）的信息。这种双域输入使网络能够解耦退化因素，将颜色/亮度信息与纹理/噪声分离。网络核心采用基于Mamba的SS2D模块，以线性复杂度捕获全局感受野和长距离依赖，克服了CNN和Transformer的局限性。此外，我们引入由背景光先验引导的ColorFusion模块，以高保真度恢复颜色信息。在LSUI和UIEB基准数据集上的大量实验表明，Hero-Mamba超越现有最优方法。值得注意的是，我们的模型在LSUI数据集上达到25.802 dB的PSNR和0.913的SSIM，验证了其卓越性能与泛化能力。