  Pansharpening aims to fuse a multispectral (MS) image with an associated
panchromatic (PAN) image, producing a composite image with the spectral
resolution of the former and the spatial resolution of the latter. Traditional
pansharpening methods can be ascribed to a unified detail injection context,
which views the injected MS details as the integration of PAN details and
band-wise injection gains. In this work, we design a detail injection based CNN
(DiCNN) framework for pansharpening, with the MS details being directly
formulated in end-to-end manners, where the first detail injection based CNN
(DiCNN1) mines MS details through the PAN image and the MS image, and the
second one (DiCNN2) utilizes only the PAN image. The main advantage of the
proposed DiCNNs is that they provide explicit physical interpretations and can
achieve fast convergence while achieving high pansharpening quality.
Furthermore, the effectiveness of the proposed approaches is also analyzed from
a relatively theoretical point of view. Our methods are evaluated via
experiments on real-world MS image datasets, achieving excellent performance
when compared to other state-of-the-art methods.
