标题： 几何时空谱全变分用于高光谱图像去噪和去条纹 显示英文标题

标题： Geometric Spatio-Spectral Total Variation for Hyperspectral Image Denoising and Destriping

摘要： 本文提出了一种新颖的正则化方法，称为几何时空全变分（GeoSSTV），用于高光谱（HS）图像去噪和去条纹。由于测量设备和环境的影响，HS图像不可避免地受到各种类型的噪声影响。基于全变分（TV）的正则化方法通过建模HS图像中固有的时空分段平滑性，是HS图像去噪和去条纹的有前途的方法。然而，现有的基于TV的方法基于经典的各向异性和各向同性TV，分别导致阶梯伪影和缺乏旋转不变性，使得难以准确恢复圆形结构和斜边。为了解决这个问题，GeoSSTV引入了一种几何一致的TV公式，以欧几里得方式测量所有方向上的变化。通过这种公式，GeoSSTV在去除噪声的同时保留了圆形结构和斜边。此外，我们将HS图像去噪问题表述为涉及GeoSSTV的约束凸优化问题，并开发了一种基于预条件原始对偶分裂方法的高效算法。在受混合噪声污染的HS图像上的实验结果证明了所提方法优于现有方法。 显示英文摘要

摘要： This article proposes a novel regularization method, named Geometric Spatio-Spectral Total Variation (GeoSSTV), for hyperspectral (HS) image denoising and destriping. HS images are inevitably affected by various types of noise due to the measurement equipment and environment. Total Variation (TV)-based regularization methods that model the spatio-spectral piecewise smoothness inherent in HS images are promising approaches for HS image denoising and destriping. However, existing TV-based methods are based on classical anisotropic and isotropic TVs, which cause staircase artifacts and lack rotation invariance, respectively, making it difficult to accurately recover round structures and oblique edges. To address this issue, GeoSSTV introduces a geometrically consistent formulation of TV that measures variations across all directions in a Euclidean manner. Through this formulation, GeoSSTV removes noise while preserving round structures and oblique edges. Furthermore, we formulate the HS image denoising problem as a constrained convex optimization problem involving GeoSSTV and develop an efficient algorithm based on a preconditioned primal-dual splitting method. Experimental results on HS images contaminated with mixed noise demonstrate the superiority of the proposed method over existing approaches.