Hero-Mamba：基于Mamba的双域学习用于水下图像增强
Hero-Mamba: Mamba-based Dual Domain Learning for Underwater Image Enhancement
AI总结：
本文提出Hero-Mamba，一种基于Mamba的网络，通过同时处理空间域和光谱域实现高效的水下图像增强，解决传统方法在长距离依赖建模和计算效率上的不足。
摘要：
水下图像常因光线吸收和散射而出现严重退化，如颜色失真、低对比度和模糊细节。尽管基于学习的方法如CNN和Transformer已显示出潜力，但它们面临关键限制：CNN难以建模非均匀退化的长距离依赖，而Transformer的二次计算复杂度使其在高分辨率图像处理中效率低下。为解决这些挑战，我们提出Hero-Mamba，一种新型基于Mamba的网络，实现了高效的双域学习用于水下图像增强。我们的方法独特地并行处理空间域（RGB图像）和光谱域（FFT组件）。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/噪声分开。网络核心利用基于Mamba的SS2D块来捕捉全局感受野和长距离依赖，具有线性复杂度，克服了CNN和Transformer的限制。此外，我们引入了受背景光先验指导的ColorFusion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优于现有最先进方法。值得注意的是，我们的模型在LSUI上实现了25.802的PSNR和0.913的SSIM，验证了其优越的性能和泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
