Model based pansharpening method based on TV and MTF deblurring

In the past two decades, many methods have been proposed to fuse low resolution multispectral (MS) and high resolution panchromatic (Pan) images, i.e., pansharpening. Two large families of such methods are component substitution (CS) and multiresolution analysis methods (MRA). We develop a model based method for pansharpening based on minimizing a cost function which includes a data fidelity term, a detail injection term and a total variation (TV) term. The model takes into account the modulation transfer function (MTF) and spectral response of the sensor. The resulting iterative method not only sharpens the MS image with details from the Pan image but is also able to extract important information from the MS image itself via MTF-based deconvolution. We compare the proposed method to a number of state-of-the-art CS and MRA pansharpening methods using a real WorldView-2 dataset and show that it gives excellent results with details that all the CS and MRA methods can not extract.