Pyramid Dual Domain Injection Network for Pan-sharpening

Abstract

Pan-sharpening, a panchromatic image guided low-spatial-resolution multi-spectral super-resolution task, aims to reconstruct the missing high-frequency information of high-resolution multi-spectral counterpart. Although the inborn connection with frequency domain, existing pan-sharpening research has almost investigated the potential solution upon frequency domain, thus limiting the model performance improvement. To this end, we first revisit the degradation process of pan-sharpening in Fourier space, and then devise a Pyramid Dual Domain Injection pan-sharpening Network upon the above observation by fully exploring and exploiting the distinguished information in both the spatial and frequency domains. Specifically, the proposed network is organized with multi-scale U-shape manner and composed by two core parts: a spatial guidance pyramid sub-network for fusing local spatial information and a frequency guidance pyramid sub-network for fusing global frequency domain information, thus encouraging dual-domain complementary learning. In this way, the model can capture multi-scale dual-domain information to enable generating high-quality pan-sharpening results. Quantitative and qualitative experiments over multiple datasets demonstrate that our method performs the best against other state-of-the-art ones and comprises a strong generalization ability for real-world scenes.