  Recent years have seen significant advancements in image restoration, largely
attributed to the development of modern deep neural networks, such as CNNs and
Transformers. However, existing restoration backbones often face the dilemma
between global receptive fields and efficient computation, hindering their
application in practice. Recently, the Selective Structured State Space Model,
especially the improved version Mamba, has shown great potential for long-range
dependency modeling with linear complexity, which offers a way to resolve the
above dilemma. However, the standard Mamba still faces certain challenges in
low-level vision such as local pixel forgetting and channel redundancy. In this
work, we introduce a simple but effective baseline, named MambaIR, which
introduces both local enhancement and channel attention to improve the vanilla
Mamba. In this way, our MambaIR takes advantage of the local pixel similarity
and reduces the channel redundancy. Extensive experiments demonstrate the
superiority of our method, for example, MambaIR outperforms SwinIR by up to
0.45dB on image SR, using similar computational cost but with a global
receptive field. Code is available at \url{https://github.com/csguoh/MambaIR}.
