ColorMamba：基于Mamba的高质量近红外（NIR）到RGB光谱翻译
ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba
AI总结：
针对近红外到RGB光谱翻译中模型难以兼顾大感受野与效率的问题，提出引入可学习填充标记、局部卷积增强等改进的ColorMamba骨干网络，结合HSV多尺度引导，PSNR较现有最优方法提升1.02。
摘要：
由于跨域复杂性，将近红外（NIR）转换为可见光谱是一项具有挑战性的任务。现有模型难以在大感受野与计算效率之间取得平衡，限制了实际应用。尽管选择性结构化状态空间模型（尤其是改进版本Mamba）凭借线性复杂度捕获长程依赖关系，在生成任务中表现出色，但其将2D图像转换为1D序列的默认方法忽略了局部上下文。在本研究中，我们提出了一种简单而有效的骨干网络ColorMamba，首次将Mamba引入光谱翻译任务。为探索全局长程依赖与局部上下文以实现高效光谱翻译，我们引入可学习填充标记，以增强图像边界的区分度并避免序列模型内部出现潜在混淆。此外，我们设计了局部卷积增强模块和智能体注意力机制，对原始Mamba进行改进。同时，我们利用HSV颜色空间在重建过程中提供多尺度引导，以实现更精准的光谱翻译。大量实验表明，与当前最优方法相比，我们的ColorMamba在峰值信噪比（PSNR）指标上提升了1.02。我们的代码可在https://github.com/AlexYangxx/ColorMamba获取。
英文摘要：
Translating NIR to the visible spectrum is challenging due to cross-domain complexities. Current models struggle to balance a broad receptive field with computational efficiency, limiting practical use. Although the Selective Structured State Space Model, especially the improved version, Mamba, excels in generative tasks by capturing long-range dependencies with linear complexity, its default approach of converting 2D images into 1D sequences neglects local context. In this work, we propose a simple but effective backbone, dubbed ColorMamba, which first introduces Mamba into spectral translation tasks. To explore global long-range dependencies and local context for efficient spectral translation, we introduce learnable padding tokens to enhance the distinction of image boundaries and prevent potential confusion within the sequence model. Furthermore, local convolutional enhancement and agent attention are designed to improve the vanilla Mamba. Moreover, we exploit the HSV color to provide multi-scale guidance in the reconstruction process for more accurate spectral translation. Extensive experiments show that our ColorMamba achieves a 1.02 improvement in terms of PSNR compared with the state-of-the-art method. Our code is available at https://github.com/AlexYangxx/ColorMamba.
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。
