基于补偿的字典迁移用于相似多光谱图像光谱超分辨率
Compensation based Dictionary Transfer for Similar Multispectral Image Spectral Super-resolution
AI总结：
本文提出一种基于补偿矩阵的字典迁移方法，通过相似性约束将训练域光谱字典迁移至超分辨率域并优化稀疏系数矩阵，有效提升了相似场景多光谱图像的光谱超分辨率重建精度。
摘要：
利用从几对相似场景的多光谱和高光谱图像中学习到的光谱字典，仅用单幅多光谱图像即可重建所需的高光谱图像。然而，相似场景与所需高光谱图像之间的差异，使得难以将训练域的光谱字典直接应用于任务域。为此，本文提出一种基于补偿矩阵的字典迁移方法，用于相似场景多光谱图像光谱超分辨率，试图重建更准确的高空间分辨率高光谱图像。具体而言，通过使用带相似性约束的补偿矩阵建立光谱字典迁移方案，将训练域中学习到的光谱字典迁移到光谱超分辨率域。随后，在稀疏和低秩约束下优化稀疏系数矩阵。在来自不同场景的两个AVIRIS数据集上的实验结果表明，所提方法优于其他相关SOTA方法。
英文摘要：
Utilizing a spectral dictionary learned from a couple of similar-scene multi- and hyperspectral image, it is possible to reconstruct a desired hyperspectral image only with one single multispectral image. However, the differences between the similar scene and the desired hyperspectral image make it difficult to directly apply the spectral dictionary from the training domain to the task domain. To this end, a compensation matrix based dictionary transfer method for the similar-scene multispectral image spectral super-resolution is proposed in this paper, trying to reconstruct a more accurate high spatial resolution hyperspectral image. Specifically, a spectral dictionary transfer scheme is established by using a compensation matrix with similarity constraint, to transfer the spectral dictionary learned in the training domain to the spectral super-resolution domain. Subsequently, the sparse coefficient matrix is optimized under sparse and low-rank constraints. Experimental results on two AVIRIS datasets from different scenes indicate that, the proposed method outperforms other related SOTA methods.
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。
