Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based
  Transformer Network for Remote Sensing Image Super-Resolution
Yuting Lu Lingtong Min Binglu Wang Le Zheng Xiaoxu Wang Yongqiang Zhao Teng Long
Remote sensing image super-resolution (RSISR) plays a vital role in enhancing
spatial detials and improving the quality of satellite imagery. Recently,
Transformer-based models have shown competitive performance in RSISR. To
mitigate the quadratic computational complexity resulting from global
self-attention, various methods constrain attention to a local window,
enhancing its efficiency. Consequently, the receptive fields in a single
attention layer are inadequate, leading to insufficient context modeling.
Furthermore, while most transform-based approaches reuse shallow features
through skip connections, relying solely on these connections treats shallow
and deep features equally, impeding the model's ability to characterize them.
To address these issues, we propose a novel transformer architecture called
Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based
Transformer Network (SPIFFNet) for RSISR. Our proposed model effectively
enhances global cognition and understanding of the entire image, facilitating
efficient integration of features cross-stages. The model incorporates
cross-spatial pixel integration attention (CSPIA) to introduce contextual
information into a local window, while cross-stage feature fusion attention
(CSFFA) adaptively fuses features from the previous stage to improve feature
expression in line with the requirements of the current stage. We conducted
comprehensive experiments on multiple benchmark datasets, demonstrating the
superior performance of our proposed SPIFFNet in terms of both quantitative
metrics and visual quality when compared to state-of-the-art methods.