  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}.
