Many recent studies have shown that the adaptive total variation regularization has the advantage of better preserving local features of images compared with the celebrated total variation regularization. On the other hand, the superpixel-based weighted nuclear norm can compensate for the shortcomings of the superpixel-based standard nuclear norm, assigning different weights to singular values and improving flexibility. Inspired by these two factors, we propose two new hyperspectral sparse fusion models related to the adaptive total variation regularization and superpixel-based weighted nuclear norm. Furthermore, we design the alternating direction method of multipliers (ADMM) to efficiently solve the proposed models, with complexity and convergence analyses. Experimental results demonstrate that the proposed methods outperform several state-of-the-art methods both numerically and visually.

中文翻译：

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使用自适应全变分正则化和基于超像素的加权核范数的高光谱稀疏融合

最近的许多研究表明，与著名的全变分正则化相比，自适应全变分正则化具有更好地保留图像局部特征的优点。另一方面，基于超像素的加权核范数可以弥补基于超像素的标准核范数的缺点，为奇异值分配不同的权重，提高灵活性。受这两个因素的启发，我们提出了两种新的高光谱稀疏融合模型，涉及自适应全变分正则化和基于超像素的加权核范数。此外，我们设计了乘子交替方向法（ADMM）来有效地求解所提出的模型，并进行复杂性和收敛性分析。实验结果表明，所提出的方法在数值和视觉上都优于几种最先进的方法。