  The challenge of the cloud removal task can be alleviated with the aid of
Synthetic Aperture Radar (SAR) images that can penetrate cloud cover. However,
the large domain gap between optical and SAR images as well as the severe
speckle noise of SAR images may cause significant interference in SAR-based
cloud removal, resulting in performance degeneration. In this paper, we propose
a novel global-local fusion based cloud removal (GLF-CR) algorithm to leverage
the complementary information embedded in SAR images. Exploiting the power of
SAR information to promote cloud removal entails two aspects. The first, global
fusion, guides the relationship among all local optical windows to maintain the
structure of the recovered region consistent with the remaining cloud-free
regions. The second, local fusion, transfers complementary information embedded
in the SAR image that corresponds to cloudy areas to generate reliable texture
details of the missing regions, and uses dynamic filtering to alleviate the
performance degradation caused by speckle noise. Extensive evaluation
demonstrates that the proposed algorithm can yield high quality cloud-free
images and outperform state-of-the-art cloud removal algorithms with a gain
about 1.7dB in terms of PSNR on SEN12MS-CR dataset.
