Unsupervised Pan-Sharpening via Mutually Guided Detail Restoration
DOI:
https://doi.org/10.1609/aaai.v38i4.28125Keywords:
CV: Low Level & Physics-based VisionAbstract
Pan-sharpening is a task that aims to super-resolve the low-resolution multispectral (LRMS) image with the guidance of a corresponding high-resolution panchromatic (PAN) image. The key challenge in pan-sharpening is to accurately modeling the relationship between the MS and PAN images. While supervised deep learning methods are commonly employed to address this task, the unavailability of ground-truth severely limits their effectiveness. In this paper, we propose a mutually guided detail restoration method for unsupervised pan-sharpening. Specifically, we treat pan-sharpening as a blind image deblurring task, in which the blur kernel can be estimated by a CNN. Constrained by the blur kernel, the pan-sharpened image retains spectral information consistent with the LRMS image. Once the pan-sharpened image is obtained, the PAN image is blurred using a pre-defined blur operator. The pan-sharpened image, in turn, is used to guide the detail restoration of the blurred PAN image. By leveraging the mutual guidance between MS and PAN images, the pan-sharpening network can implicitly learn the spatial relationship between the two modalities. Extensive experiments show that the proposed method significantly outperforms existing unsupervised pan-sharpening methods.Downloads
Published
2024-03-24
How to Cite
Lin, H., Dong, Y., Ding, X., Liu, T., & Liu, Y. (2024). Unsupervised Pan-Sharpening via Mutually Guided Detail Restoration. Proceedings of the AAAI Conference on Artificial Intelligence, 38(4), 3386-3394. https://doi.org/10.1609/aaai.v38i4.28125
Issue
Section
AAAI Technical Track on Computer Vision III