Recent years have witnessed great progress in image restoration thanks to the
advancements in modern deep neural networks e.g. Convolutional Neural Network
and Transformer. However, existing restoration backbones are usually limited
due to the inherent local reductive bias or quadratic computational complexity.
Recently, Selective Structured State Space Model e.g., Mamba, has shown great
potential for long-range dependencies modeling with linear complexity, but it
is still under-explored in low-level computer vision. In this work, we
introduce a simple but strong benchmark model, named MambaIR, for image
restoration. In detail, we propose the Residual State Space Block as the core
component, which employs convolution and channel attention to enhance the
capabilities of the vanilla Mamba. In this way, our MambaIR takes advantage of
local patch recurrence prior as well as channel interaction to produce
restoration-specific feature representation. Extensive experiments demonstrate
the superiority of our method, for example, MambaIR outperforms
Transformer-based baseline SwinIR by up to 0.36dB, using similar computational
cost but with a global receptive field. Code is available at
https://github.com/csguoh/MambaIR.

通过引入 Residual State Space Block 作为核心组件，该研究提出了一种名为 MambaIR 的简单而强大的基准模型，利用卷积和通道注意力增强了 vanilla Mamba 的能力，从而利用局部补丁的重复特性和通道交互产生特定于恢复的特征表示。广泛的实验证明了我们方法的优越性，例如，MambaIR 在类似计算成本但具有全局感受野的情况下，比基于 Transformer 的基准模型 SwinIR 提高了 0.36dB。