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Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution

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arxiv 2410.10140 v1 pith:OSMOZ2YS submitted 2024-10-14 cs.CV

Junbo Qiao , Jincheng Liao , Wei Li , Yulun Zhang , Yong Guo , Yi Wen , Zhangxizi Qiu , Jiao Xie
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Jie Hu Shaohui Lin
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classification cs.CV
keywords hi-mambamambahierarchicalimagemodelingscanningabilitydependency
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abstract

State Space Models (SSM), such as Mamba, have shown strong representation ability in modeling long-range dependency with linear complexity, achieving successful applications from high-level to low-level vision tasks. However, SSM's sequential nature necessitates multiple scans in different directions to compensate for the loss of spatial dependency when unfolding the image into a 1D sequence. This multi-direction scanning strategy significantly increases the computation overhead and is unbearable for high-resolution image processing. To address this problem, we propose a novel Hierarchical Mamba network, namely, Hi-Mamba, for image super-resolution (SR). Hi-Mamba consists of two key designs: (1) The Hierarchical Mamba Block (HMB) assembled by a Local SSM (L-SSM) and a Region SSM (R-SSM) both with the single-direction scanning, aggregates multi-scale representations to enhance the context modeling ability. (2) The Direction Alternation Hierarchical Mamba Group (DA-HMG) allocates the isomeric single-direction scanning into cascading HMBs to enrich the spatial relationship modeling. Extensive experiments demonstrate the superiority of Hi-Mamba across five benchmark datasets for efficient SR. For example, Hi-Mamba achieves a significant PSNR improvement of 0.29 dB on Manga109 for $\times3$ SR, compared to the strong lightweight MambaIR.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PCMamba: Physics-Informed Cross-Modal State Space Model for Dual-Camera Compressive Hyperspectral Imaging

    eess.IV 2025-05 conditional novelty 6.0 of 10

    PCMamba reports state-of-the-art dual-camera hyperspectral reconstruction by using a learned temperature-emissivity-texture decomposition inside a Mamba-based network.

  2. FRN: Fractal-Based Recursive Spectral Reconstruction Network

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A recursive network that builds hyperspectral bands progressively from RGB using a shared atomic module reports state-of-the-art reconstruction on CAVE and Harvard with only 0.30M parameters.

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