论文标题
强大的高光谱图像融合与同时指导图像通过受约束的凸优化构造
Robust Hyperspectral Image Fusion with Simultaneous Guide Image Denoising via Constrained Convex Optimization
论文作者
论文摘要
本文提出了一种基于凸优化的新型高空间分辨率高光谱（HR-HS）图像估计方法。该方法假定空间分辨率HS（LR-HS）图像和指导图像作为观测值，其中两个观察结果都被噪声污染。我们的方法同时估算了HR-HS图像和无噪声指南图像，因此该方法即使被大噪声污染了指南图像中的空间信息也可以利用空间信息。提出的估计问题通过正则化采用混合空间 - 光谱总变化，并评估HR-HS和指南图像之间的边缘相似性，以有效地对指南图像中的HR-HS图像和空间详细信息信息有效地使用APRIORI知识。为了有效地解决该问题，我们采用了一种原始的二重分裂方法。实验证明了我们的方法的性能以及对几种现有方法的优势。
The paper 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 spatio-spectral total variation as regularization and evaluates the edge similarity between HR-HS and guide images to effectively use apriori 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.
