  Satellite imagery analysis plays a pivotal role in remote sensing; however,
information loss due to cloud cover significantly impedes its application.
Although existing deep cloud removal models have achieved notable outcomes,
they scarcely consider contextual information. This study introduces a
high-performance cloud removal architecture, termed Progressive Multi-scale
Attention Autoencoder (PMAA), which concurrently harnesses global and local
information to construct robust contextual dependencies using a novel
Multi-scale Attention Module (MAM) and a novel Local Interaction Module (LIM).
PMAA establishes long-range dependencies of multi-scale features using MAM and
modulates the reconstruction of fine-grained details utilizing LIM, enabling
simultaneous representation of fine- and coarse-grained features at the same
level. With the help of diverse and multi-scale features, PMAA consistently
outperforms the previous state-of-the-art model CTGAN on two benchmark
datasets. Moreover, PMAA boasts considerable efficiency advantages, with only
0.5% and 14.6% of the parameters and computational complexity of CTGAN,
respectively. These comprehensive results underscore PMAA's potential as a
lightweight cloud removal network suitable for deployment on edge devices to
accomplish large-scale cloud removal tasks. Our source code and pre-trained
models are available at https://github.com/XavierJiezou/PMAA.
