Adaptively Learning Low-high Frequency Information Integration for Pan-sharpening

Abstract

Pan-sharpening aims to generate high-spatial resolution multi-spectral (MS) image by fusing high-spatial resolution panchromatic (PAN) image and its corresponding low-spatial resolution MS image. Despite the remarkable progress, most existing pan-sharpening methods only work in the spatial domain and rarely explore the potential solutions in the frequency domain. In this paper, we propose a novel pan-sharpening framework by adaptively learning low-high frequency information integration in the spatial and frequency dual domains. It consists of three key designs: mask prediction sub-network, low-frequency learning sub-network and high-frequency learning sub-network. Specifically, the first is responsible for measuring the modality-aware frequency information difference of PAN and MS images and further predicting the low-high frequency boundary in the form of a two-dimensional mask. In view of the mask, the second adaptively picks out the corresponding low-frequency components of different modalities and then restores the expected low-frequency one by spatial and frequency dual domains information integration while the third combines the above refined low-frequency and the original high-frequency for the latent high-frequency reconstruction. In this way, the low-high frequency information is adaptively learned, thus leading to the pleasing results. Extensive experiments validate the effectiveness of the proposed network and demonstrate the favorable performance against other state-of-the-art methods. The source code will be released at https://github.com/manman1995/pansharpening.