Normalization-based Feature Selection and Restitution for Pan-sharpening

Pan-sharpening is essentially a panchromatic (PAN) image-guided low-spatial resolution MS image super-resolution problem. The commonly challenging issue of pan-sharpening is how to correctly select consistent features and propagate them, and properly handle inconsistent ones between PAN and MS modalities. To solve this issue, we propose a Normalization-based Feature Selection and Restitution mechanism, which is capable of filtering out the inconsistent features and promoting to learn the consistent ones. Specifically, we first modulate the PAN feature as the MS style in feature space by AdaIN operation . However, such operation inevitably removes the favorable features. We thus propose to distill the effective information from the removed part and restitute it back to the modulated part. To better distillation, we enforce a contrastive learning constraint to close the distance between the restituted feature and the ground truth, and push the removed part away from the ground truth. In this way, the consistent features of PAN images are correctly selected and the inconsistent ones are filtered out, thus relieving the over-transferred artifacts in the process of PAN-guided MS super-resolution. Extensive experiments validate the effectiveness of the proposed network and demonstrate its favorable performance against other state-of-the-art methods. The source code will be released at https://github.com/manman1995/pansharpening.