Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution
arxiv(2024)
摘要
Recent progress in remote sensing image (RSI) super-resolution (SR) has
exhibited remarkable performance using deep neural networks, e.g.,
Convolutional Neural Networks and Transformers. However, existing SR methods
often suffer from either a limited receptive field or quadratic computational
overhead, resulting in sub-optimal global representation and unacceptable
computational costs in large-scale RSI. To alleviate these issues, we develop
the first attempt to integrate the Vision State Space Model (Mamba) for RSI-SR,
which specializes in processing large-scale RSI by capturing long-range
dependency with linear complexity. To achieve better SR reconstruction,
building upon Mamba, we devise a Frequency-assisted Mamba framework, dubbed
FMSR, to explore the spatial and frequent correlations. In particular, our FMSR
features a multi-level fusion architecture equipped with the Frequency
Selection Module (FSM), Vision State Space Module (VSSM), and Hybrid Gate
Module (HGM) to grasp their merits for effective spatial-frequency fusion.
Recognizing that global and local dependencies are complementary and both
beneficial for SR, we further recalibrate these multi-level features for
accurate feature fusion via learnable scaling adaptors. Extensive experiments
on AID, DOTA, and DIOR benchmarks demonstrate that our FMSR outperforms
state-of-the-art Transformer-based methods HAT-L in terms of PSNR by 0.11 dB on
average, while consuming only 28.05
complexity, respectively.
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