标题： 从RGB图像中释放相关性和连续性用于高光谱重建 显示英文标题

标题： Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images

摘要： 从RGB图像重建高光谱图像（HSI）可以在较低成本下获得高空间分辨率的HSI，显示出显著的应用潜力。 本文揭示了光谱特性的局部相关性和全局连续性对于HSI重建任务至关重要。 因此，我们全面探索了这些跨光谱关系，并提出了一种用于从RGB图像重建HSI的相关性和连续性网络（CCNet）。 对于局部光谱的相关性，我们引入了分组光谱相关性建模（GrSCM）模块，该模块在局部范围内高效建立光谱波段相似性。 对于全局光谱的连续性，我们设计了邻域光谱连续性建模（NeSCM）模块，该模块使用记忆单元递归地建模全局层面的渐进变化特性。 为了探索这两个模块的内在互补性，我们设计了块状自适应融合（PAF）模块，以块状自适应的方式高效地将全局连续性特征整合到光谱特征中。 这些创新提高了重建HSI的质量。 我们在主流数据集NTIRE2022和NTIRE2020上对光谱重建任务进行了全面的比较和消融实验。 与当前先进的光谱重建算法相比，我们设计的算法实现了最先进的（SOTA）性能。 显示英文摘要

摘要： Reconstructing Hyperspectral Images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals that local correlation and global continuity of the spectral characteristics are crucial for HSI reconstruction tasks. Therefore, we fully explore these inter-spectral relationships and propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images. For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module, which efficiently establishes spectral band similarity within a localized range. For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module, which employs memory units to recursively model the progressive variation characteristics at the global level. In order to explore the inherent complementarity of these two modules, we design the Patch-wise Adaptive Fusion (PAF) module to efficiently integrate global continuity features into the spectral features in a patch-wise adaptive manner. These innovations enhance the quality of reconstructed HSI. We perform comprehensive comparison and ablation experiments on the mainstream datasets NTIRE2022 and NTIRE2020 for the spectral reconstruction task. Compared to the current advanced spectral reconstruction algorithms, our designed algorithm achieves State-Of-The-Art (SOTA) performance.