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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >MTF-Based Deblurring Using a Wiener Filter for CS and MRA Pansharpening Methods
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MTF-Based Deblurring Using a Wiener Filter for CS and MRA Pansharpening Methods

机译：使用Wiener滤波器的基于MTF的去模糊，用于CS和MRA全屏锐化方法

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摘要

Pansharpening is the fusion of low-resolution multispectral (MS) images and high-resolution panchromatic (PAN) images to yield a high-resolution MS image. The component substitution (CS) and multiresolution analysis (MRA) methods are usually computationally efficient, making them able to handle large datasets. However, these methods often produce images that suffer from spectral and spatial distortions. The CS and MRA methods can be described using general injection schemes where details extracted from the PAN image, modulated by a band-dependent gain constant, are added to the MS image, which has been interpolated to the size of the PAN image. In this paper, we propose a simple modification of these schemes where the interpolated MS image is replaced by its deblurred version, where the deblurring kernel is matched to the modulation transfer function (MTF) of the MS sensor. This can significantly enhance the quality of the fused image. Using two real datasets and one simulated dataset, our experimental results show that using the proposed preprocessing method can significantly increase both the spectral and spatial quality of the fused image according to quantitative quality metrics.
机译：全色锐化是将低分辨率多光谱（MS）图像和高分辨率全色（PAN）图像融合在一起以生成高分辨率MS图像。组件替换（CS）和多分辨率分析（MRA）方法通常在计算上很高效，从而使其能够处理大型数据集。但是，这些方法通常会产生遭受光谱和空间失真的图像。可以使用一般注入方案来描述CS和MRA方法，其中将从PAN图像中提取的细节（由带相关增益常数调制）添加到MS图像中，该图像已内插到PAN图像的大小。在本文中，我们提出了对这些方案的简单修改，其中将内插的MS图像替换为其去模糊的版本，其中去模糊的内核与MS传感器的调制传递函数（MTF）相匹配。这可以显着提高融合图像的质量。通过使用两个真实数据集和一个模拟数据集，我们的实验结果表明，根据定量质量指标，使用所提出的预处理方法可以显着提高融合图像的光谱和空间质量。

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