IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model
<|reference_start|>IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model: Infrared (IR) image super-resolution faces challenges from homogeneous background pixel distributions and sparse target regions, requiring models that effectively handle long-range dependencies and capture detailed local-global information. Recent advancements in Mamba-based (Selective Structured State Space Model) models, employing state space models, have shown significant potential in visual tasks, suggesting their applicability for IR enhancement. In this work, we introduce IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model, a novel Mamba-based model designed specifically for IR image super-resolution. This model enhances the restoration of context-sparse target details through its advanced dependency modeling capabilities. Additionally, a new wavelet transform feature modulation block improves multi-scale receptive field representation, capturing both global and local information efficiently. Comprehensive evaluations confirm that IRSRMamba outperforms existing models on multiple benchmarks. This research advances IR super-resolution and demonstrates the potential of Mamba-based models in IR image processing. Code are available at \url{https://github.com/yongsongH/IRSRMamba}.<|reference_end|>
@article{huang2024irsrmamba:,
    title={IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet
  Transform Feature Modulation Model},
    author={Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu and Shinichiro Omachi},
    journal={arXiv preprint arXiv:2405.09873},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09873},
    primaryClass={cs.CV eess.IV}
}