![](bg7.png)
titioned them into the train, validation, and test sets
with percentages of 0.8, 0.1, and 0.1 respectively.
The STGAN model shows the state-of-the-art perfor-
mance in our dataset. The PSNR and SSIM reach up
to 33.4 and 0.929 respectively. We hope our dataset
will be widely used, which makes more satellite data
used for further research and applications. We have
made our dataset publicly available at the following
link: https://github.com/zhumorui/SMT-CR.
Our future work will focus on dealing with im-
ages on entire images, as opposed to cropped images.
By processing entire images, we can effectively uti-
lize global spatio-temporal information, while avoid-
ing the risk of errors that may occur at the edges of
cropped images. Furthermore, we will test and com-
pare the different state of art networks on our dataset.
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