MambaFormerSR: A Lightweight Model for Remote-Sensing Image Super-Resolution

In recent years, deep learning-based method have shown impressive performance in super-resolution (SR) reconstruction tasks for remote-sensing images. However, many existing methods require significant computational resources, posing a challenge for edge devices with limited computing capabilities. To address this issue, we introduce Mamba, an emerging novel state space model (SSM), which possesses a global receptive field and linear complexity, and apply it to remote-sensing SR tasks. Furthermore, to enhance global modeling capabilities, we integrate Mamba and Transformer to develop a lightweight remote-sensing SR model named MambaFormerSR. First, we design a state space and attention fusion module (SAFM) to capture long-range spatial dependencies and fully extract image features. Subsequently, we introduce an improved feed-forward network module, named convolutional Fourier transform feed-forward network (CTFFN), which utilizes convolutions to incorporate local information and employs a frequency-based attention module to enable the model to focus on informative frequency components, further refining the features. Finally, we add an auxiliary reconstruction branch to reconstruct coarse-grained image information, enabling the network to concentrate on extracting fine-grained image details and reconstructing high-quality images. Extensive experiments have demonstrated the superiority of our approach, with our model achieving a PSNR value of 30.28 dB on the UCMerced dataset for
$\times 3$
SR with 306k parameters.