论文标题
高光谱图像denoising的图形时空传热总变异模型
Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising
论文作者
论文摘要
空间谱总变化（SSTV）模型已被广泛用作高光谱图像（HSI）的有效正规化，用于各种应用（例如混合噪声）。但是，由于SSTV统一地计算局部空间差异，因此很难消除噪声，同时保留具有细边和纹理的复杂空间结构，尤其是在高噪声强度的情况下。为了解决这个问题，我们提出了一种称为Graph-SSTV（GSSTV）的新的TV型正则化，该型图生成了一个图形，从嘈杂的HSIS中明确反映了目标HSI的空间结构，并结合了基于此图的加权空间差异操作员。此外，我们将混合降噪问题提出为涉及GSSTV的凸优化问题，并基于原始的双重分裂方法开发有效的算法来解决此问题。最后，我们通过消除混合噪声的实验与现有的HSI正则化模型相比，证明了GSSTV的有效性。源代码将在https://www.mdi.c.titech.ac.ac.jp/publications/gsstv上找到。
The spatio-spectral total variation (SSTV) model has been widely used as an effective regularization of hyperspectral images (HSI) for various applications such as mixed noise removal. However, since SSTV computes local spatial differences uniformly, it is difficult to remove noise while preserving complex spatial structures with fine edges and textures, especially in situations of high noise intensity. To solve this problem, we propose a new TV-type regularization called Graph-SSTV (GSSTV), which generates a graph explicitly reflecting the spatial structure of the target HSI from noisy HSIs and incorporates a weighted spatial difference operator designed based on this graph. Furthermore, we formulate the mixed noise removal problem as a convex optimization problem involving GSSTV and develop an efficient algorithm based on the primal-dual splitting method to solve this problem. Finally, we demonstrate the effectiveness of GSSTV compared with existing HSI regularization models through experiments on mixed noise removal. The source code will be available at https://www.mdi.c.titech.ac.jp/publications/gsstv.
