  Backprojection networks have achieved promising super-resolution performance
for nature images but not well be explored in the remote sensing image
super-resolution (RSISR) field due to the high computation costs. In this
paper, we propose a Multi-granularity Backprojection Transformer termed MBT for
RSISR. MBT incorporates the backprojection learning strategy into a Transformer
framework. It consists of Scale-aware Backprojection-based Transformer Layers
(SPTLs) for scale-aware low-resolution feature learning and Context-aware
Backprojection-based Transformer Blocks (CPTBs) for hierarchical feature
learning. A backprojection-based reconstruction module (PRM) is also introduced
to enhance the hierarchical features for image reconstruction. MBT stands out
by efficiently learning low-resolution features without excessive modules for
high-resolution processing, resulting in lower computational resources.
Experiment results on UCMerced and AID datasets demonstrate that MBT obtains
state-of-the-art results compared to other leading methods.
