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Pansharpening Based on Semiblind Deconvolution

机译：基于半盲反卷积的泛锐化

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摘要

Many powerful pansharpening approaches exploit the functional relation between the fusion of PANchromatic (PAN) and MultiSpectral (MS) images. To this purpose, the modulation transfer function of the MS sensor is typically used, being easily approximated as a Gaussian filter whose analytic expression is fully specified by the sensor gain at the Nyquist frequency. However, this characterization is often inadequate in practice. In this paper, we develop an algorithm for estimating the relation between PAN and MS images directly from the available data through an efficient optimization procedure. The effectiveness of the approach is validated both on a reduced scale data set generated by degrading images acquired by the IKONOS sensor and on full-scale data consisting of images collected by the QuickBird sensor. In the first case, the proposed method achieves performances very similar to that of the algorithm that relies upon the full knowledge of the degrading filter. In the second, it is shown to outperform several very credited state-of-the-art approaches for the extraction of the details used in the current literature.
机译：许多强大的全锐化方法都利用PANcolor（PAN）和MultiSpectral（MS）图像融合之间的功能关系。为此，通常使用MS传感器的调制传递函数，可以很容易地将其近似为高斯滤波器，其解析表达式完全由奈奎斯特频率下的传感器增益指定。但是，这种表征在实践中通常是不够的。在本文中，我们开发了一种算法，可通过有效的优化程序直接从可用数据中估算PAN和MS图像之间的关系。该方法的有效性在通过降低由IKONOS传感器获取的图像而生成的缩小比例数据集以及由QuickBird传感器收集的图像组成的全比例数据上都得到了验证。在第一种情况下，所提出的方法所实现的性能与依赖于降级滤波器的全部知识的算法非常相似。在第二篇文章中，它表现出优于几种非常有信誉的最新方法来提取当前文献中使用的细节。

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