FATSNet: transformer-based skip network with frequency attention for remote sensing image super-resolution

The growing scope of image degradation encountered in remote sensing detection has sparked significant interest in the application of deep learning methods. To address the challenges posed by high-frequency signal loss and structural distortion in reconstructed remote sensing images, a transformer-based skip network with frequency attention (named FATSNet) is proposed to improve the model's ability. The model comprises two key blocks: encoder and decoder blocks, which incorporate two novel frequency attention modules. In the encoder block, a discrete wavelet transform (DWT) module is designed to implement the representation of detailed abstract content features. The decoder block utilizes a frequency-aware…

• DS
  Doctoral Start-up Foundation of Liaoning Province

  Award: 2025-BS-0873
• KT
  Key Technologies Research and Development Program

  Awards: 2025YFE0124700, 2025YFD1500200