Multi-band semiblind deconvolution for pansharpening applications
Pansharpening consists of fusing a multispectral (MS) image together with a panchromatic (PAN) image with the aim of jointly preserving the spectral diversity of the former and the geometric richness of the latter. A crucial step in pansharpening algorithms is the detail extraction. This problem is usually addressed by the means of 2D Gaussian filters matched with the MS sensor's modulation transfer function (MTF). Nevertheless, several issues can affect this characterization (e.g. the MTF's gains at the Nyquist frequency could be not available or unreliable). Thus, in this paper we propose a technique based on blind image deblurring in order to estimate band-dependent spatial detail extraction filters by taking into consideration the possible variability of the MS spatial features along bands. The validation is carried out exploiting two real datasets acquired by the IKONOS and the QuickBird sensors.