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MambaIRv2: Attentive State Space Restoration
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MambaIRv2: Attentive State Space Restoration
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The Mamba-based image restoration backbones have recently demonstrated significant potential in balancing global reception and computational efficiency. However, the inherent causal modeling limitation of Mamba, where each token depends solely on its predecessors in the scanned sequence, restricts the full utilization of pixels across the image and thus presents new challenges in image restoration. In this work, we propose MambaIRv2, which equips Mamba with the non-causal modeling ability similar to ViTs to reach the attentive state space restoration model. Specifically, the proposed attentive state-space equation allows to attend beyond the scanned sequence and facilitate image unfolding with just one single scan. Moreover, we further introduce a semantic-guided neighboring mechanism to encourage interaction between distant but similar pixels. Extensive experiments show our MambaIRv2 outperforms SRFormer by even 0.35dB PSNR for lightweight SR even with 9.3\% less parameters and suppresses HAT on classic SR by up to 0.29dB. Code is available at https://github.com/csguoh/MambaIR.
Forward citations
Cited by 4 Pith papers
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CUST clusters patches by affinity to window tokens for efficient long-range attention and uses multi-scale residual errors to guide local high-frequency restoration, yielding higher PSNR at lower memory than prior lig...
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Edge-Efficient Image Restoration: Transformer Distillation into State-Space Models
Hybrid transformer-SSM networks found by multi-objective search run 1.17x to 3.4x faster on edge CPUs for image restoration tasks with competitive quality.
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UCAN: Unified Convolutional Attention Network for Expansive Receptive Fields in Lightweight Super-Resolution
UCAN unifies window-based spatial attention and Hedgehog Attention with a distillation-based large-kernel module and cross-layer sharing to deliver competitive PSNR at low MACs in lightweight super-resolution.
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