MTF-Based Deblurring Using a Wiener Filter for CS and MRA Pansharpening Methods

Abstract

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.