Multispectral images (MSI) contain light information in different wavelengths of objects, which convey spectral-spatial information and help improve the performance of various image processing tasks. Numerous techniques have been created to extend the application of total variation regularization in restoring multispectral images, for example, based on channel coupling and adaptive total variation regularization. The primary contribution of this paper is to propose and develop a new multispectral total variation regularization in a generalized opponent transformation domain instead of the original multispectral image domain. Here opponent transformations for multispectral images are generalized from a well-known opponent transformation for color images. We will explore the properties of generalized opponent transformation total variation (GOTTV) regularization and the corresponding optimization formula for multispectral image restoration. To evaluate the effectiveness of the new GOTTV method, we provide numerical examples that showcase its superior performance compared to existing multispectral image total variation methods, using criteria such as MPSNR and MSSIM.

翻译：多光谱图像包含物体在不同波长下的光信息，这些信息传递了光谱-空间特征，有助于提升多种图像处理任务的性能。已有众多技术被开发用于扩展全变分正则化在多光谱图像复原中的应用，例如基于通道耦合和自适应全变分正则化的方法。本文的主要贡献在于提出并发展了一种新型多光谱全变分正则化方法，该方法在广义对手变换域而非原始多光谱图像域中实施。其中，多光谱图像的对手变换是从经典的彩色图像对手变换推广而来。我们将探究广义对手变换全变分正则化的性质及其在多光谱图像复原中的对应优化公式。为评估新提出的广义对手变换全变分方法的有效性，我们提供了数值示例，展示了该方法相比现有基于全变分的多光谱图像复原方法在MPSNR和MSSIM等评价指标上的优越性能。