Pansharpening is a technique that fuses a low spatial resolution multispectral image and a
high spatial resolution panchromatic one to obtain a multispectral image with the spatial resolution
of the latter while preserving the spectral information of the multispectral image. In this paper we
propose a variational Bayesian methodology for pansharpening. The proposed methodology uses
the sensor characteristics to model the observation process and Super-Gaussian sparse image priors
on the expected characteristics of the pansharpened image. The pansharpened image, as well as all
model and variational parameters, are estimated within the proposed methodology. Using real and
synthetic data, the quality of the pansharpened images is assessed both visually and quantitatively
and compared with other pansharpening methods. Theoretical and experimental results demonstrate
the effectiveness, efficiency, and flexibility of the proposed formulation.




中文翻译：

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具有超高斯稀疏图像先验的变分贝叶斯Pansharpening。

Pansharpening是一种融合低空间分辨率多光谱图像和
高空间分辨率全色图像的技术，以获得具有
后者空间分辨率的多光谱图像，同时保留了多光谱图像的光谱信息。在本文中，我们
提出了一种变分贝叶斯方法进行泛锐化。所提出的方法使用
传感器特征来对观察过程建模，并
根据全锐化图像的预期特征对超高斯稀疏图像进行先验建模。
在所提出的方法中估计了锐化的图像以及所有模型和变化参数。使用实数和
从合成数据来看，可以通过视觉和定量方式评估全貌图像的质量，
并与其他全貌方法进行比较。理论和实验结果证明
了所提出配方的有效性，效率和灵活性。