In this paper, a new method was investigated to enhance remote sensing images by alleviating the point spread function (PSF) effect. The PSF effect exists ubiquitously in remotely sensed imagery. As a result, image quality is greatly affected, and this imposes a fundamental limit on the amount of information captured in remotely sensed images. A geostatistical filter was proposed to enhance image quality based on a downscaling-then-upscaling scheme. The difference between this method and previous methods is that the PSF is represented by breaking the pixel down into a series of sub-pixels, facilitating downscaling using the PSF and then upscaling using a square-wave response. Thus, the sub-pixels allow disaggregation as an attempt to remove the PSF effect. Experimental results on simulated and real data sets both suggest that the proposed filter can enhance the original images by reducing the PSF effect and quantify the extent to which this is possible. The predictions using the new method outperform the original coarse PSF-contaminated imagery as well as a benchmark method. The proposed method represents a new solution to compensate for the limitations introduced by remote sensors (i.e., hardware) using computer techniques (i.e., software). The method has widespread application value, particularly for applications based on remote sensing image analysis.

中文翻译：

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用于遥感图像增强的地统计过滤器

本文研究了一种通过减轻点扩展函数（PSF）效果来增强遥感图像的新方法。PSF效果普遍存在于遥感影像中。结果，图像质量受到极大影响，并且这对在遥感图像中捕获的信息量强加了基本限制。提出了一种地统计学过滤器，以基于缩小然后放大的方案来增强图像质量。此方法与先前方法之间的区别在于，PSF的表示方式是将像素分解为一系列子像素，使用PSF便于按比例缩小，然后使用方波响应来按比例放大。因此，子像素允许进行分解，以消除PSF效应。在模拟数据集和真实数据集上的实验结果均表明，所提出的滤镜可以通过减少PSF效应来增强原始图像，并量化这种可能性的程度。使用新方法进行的预测优于原始的受PSF污染的粗图像，也优于基准方法。所提出的方法代表一种新的解决方案，以补偿使用计算机技术（即软件）的远程传感器（即硬件）引入的限制。该方法具有广泛的应用价值，特别是对于基于遥感图像分析的应用。所提出的方法代表一种新的解决方案，以补偿使用计算机技术（即软件）的远程传感器（即硬件）引入的限制。该方法具有广泛的应用价值，特别是对于基于遥感图像分析的应用。所提出的方法代表一种新的解决方案，以补偿使用计算机技术（即软件）的远程传感器（即硬件）引入的限制。该方法具有广泛的应用价值，特别是对于基于遥感图像分析的应用。