Hyperspectral imaging has become a significant source of valuable data for astronomers over the past decades. Current instrumental and observing time constraints allow direct acquisition of multispectral images, with high spatial but low spectral resolution, and hyperspectral images, with low spatial but high spectral resolution. To enhance scientific interpretation of the data, we propose a data fusion method which combines the benefits of each image to recover a high spatio-spectral resolution datacube. The proposed inverse problem accounts for the specificities of astronomical instruments, such as spectrally variant blurs. We provide a fast implementation by solving the problem in the frequency domain and in a low-dimensional subspace to efficiently handle the convolution operators as well as the high dimensionality of the data. We conduct experiments on a realistic synthetic dataset of simulated observation of the upcoming James Webb Space Telescope, and we show that our fusion algorithm outperforms state-of-the-art methods commonly used in remote sensing for Earth observation.

中文翻译：

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光谱变化空间模糊下的高光谱和多光谱图像融合——在高维红外天文成像中的应用

在过去的几十年中，高光谱成像已成为天文学家宝贵数据的重要来源。当前的仪器和观测时间限制允许直接获取具有高空间但低光谱分辨率的多光谱图像和具有低空间但高光谱分辨率的高光谱图像。为了增强对数据的科学解释，我们提出了一种数据融合方法，该方法结合了每幅图像的优点，以恢复高空间光谱分辨率数据立方体。提出的逆问题解释了天文仪器的特殊性，例如光谱变化模糊。我们通过在频域和低维子空间中解决问题来提供快速实现，以有效地处理卷积算子以及数据的高维。