MambaIR: A Simple Baseline for Image Restoration with State-Space Model
CoRR(2024)
摘要
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
.
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