Robust Hyperspectral Image Fusion With Simultaneous Guide Image Denoising via Constrained Convex Optimization
Robust Hyperspectral Image Fusion With Simultaneous Guide Image Denoising via Constrained Convex Optimization
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DOI：
10.1109/tgrs.2022.3224480
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发表时间：
2022-09
影响因子：
8.2
通讯作者：
Saori Takeyama;Shunsuke Ono
中科院分区：
文献类型：
--
作者：
Saori Takeyama;Shunsuke Ono
提出了一种基于凸优化的高空间分辨率高光谱图像估计方法。该方法假设一个低空间分辨率HS（LR-HS）的图像和指导图像的意见，其中两个意见被噪声污染。我们的方法同时估计HR-HS图像和无噪声的引导图像，因此该方法可以利用引导图像中的空间信息，即使它被严重的噪声污染。该方法采用混合空间谱全变分作为正则化方法，通过评估HR-HS图像和引导图像之间的边缘相似性，有效利用HR-HS图像的先验知识和引导图像的空间细节信息。为了有效地解决这个问题，我们采用原始-对偶分裂方法。实验结果表明，我们的方法的性能和优势，比现有的几种方法。
This article proposes a new high spatial resolution hyperspectral (HR-HS) image estimation method based on convex optimization. The method assumes a low spatial resolution HS (LR-HS) image and a guide image as observations, where both observations are contaminated by noise. Our method simultaneously estimates an HR-HS image and a noiseless guide image, so the method can utilize spatial information in a guide image even if it is contaminated by heavy noise. The proposed estimation problem adopts hybrid spatiospectral total variation as regularization and evaluates the edge similarity between HR-HS and guide images to effectively use a priori knowledge on an HR-HS image and spatial detail information in a guide image. To efficiently solve the problem, we apply a primal-dual splitting method. Experiments demonstrate the performance of our method and the advantage over several existing methods.