  During the acquisition of satellite images, there is generally a trade-off
between spatial resolution and temporal resolution (acquisition frequency) due
to the onboard sensors of satellite imaging systems. High-resolution satellite
images are very important for land crop monitoring, urban planning, wildfire
management and a variety of applications. It is a significant yet challenging
task to achieve high spatial-temporal resolution in satellite imaging. With the
advent of diffusion models, we can now learn strong generative priors to
generate realistic satellite images with high resolution, which can be utilized
to promote the super-resolution task as well. In this work, we propose a novel
diffusion-based fusion algorithm called \textbf{SatDiffMoE} that can take an
arbitrary number of sequential low-resolution satellite images at the same
location as inputs, and fuse them into one high-resolution reconstructed image
with more fine details, by leveraging and fusing the complementary information
from different time points. Our algorithm is highly flexible and allows
training and inference on arbitrary number of low-resolution images.
Experimental results show that our proposed SatDiffMoE method not only achieves
superior performance for the satellite image super-resolution tasks on a
variety of datasets, but also gets an improved computational efficiency with
reduced model parameters, compared with previous methods.
