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arXiv · 2310.12507

Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution

Jinglei Hao·Wukai Li·Binglu Wang·Shunzhou Wang·Yuting Lu·Ning Li·Yongqiang Zhao
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Abstract

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.

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Jinglei Hao, Wukai Li, Binglu Wang, Shunzhou Wang, Yuting Lu, Ning Li, Yongqiang Zhao. 2023-10-19. Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution. https://arxiv.org/abs/2310.12507

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