High-Frequency Feature Transfer for Multispectral Image Super-Resolution

Low-resolution characteristics of multispectral images restrict their usability. Various approaches (e.g., single-image super-resolution reconstruction (SISR) and pansharpening method) have been proposed to enrich the spatial details of low-resolution multispectral images (LRMSs) to obtain high-resolution ones. While the pansharpening method inevitably depends on panchromatic (PAN) images, which limits its application scenarios, SISR does not need auxiliary images, yet the blurred edge details within the reconstructed super-resolution multispectral (SRMS) images still remain a big challenge. In this work, we propose a novel high-frequency feature transfer (HFFT-PAN) method for multispectral image super-resolution to tackle the above drawbacks. Specifically, we first exploit the inherent low-frequency features among LRMS images to facilitate the extraction of high-frequency features from PAN images. After that, the high-frequency features from PAN images are transferred to the reconstruction procedure of multispectral images so that the SRMS images can not only benefit from the edge detail information from PAN images but also avoid the utilization of any PAN images during inference. Moreover, we employ an additional contrastive loss during training to ensure the fidelity of the generated SRMS images. Qualitative and quantitative evaluations exhibit that the proposed method performs favorably against state-of-the-art methods. The model and code are available at https://github.com/wx0110wx/HFFT.