Larger Hausdorff Dimension in Scanning Pattern Facilitates Mamba-Based Methods in Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2510.26001v2
• Date: Fri, 31 Oct 2025 01:41:45 GMT
• Title: Larger Hausdorff Dimension in Scanning Pattern Facilitates Mamba-Based Methods in Low-Light Image Enhancement
• Authors: Xinhua Wang, Caibo Feng, Xiangjun Fu, Chunxiao Liu,
• Abstract summary: We propose an innovative enhancement to the Mamba framework by increasing the Hausdorff dimension of its scanning pattern through a novel Hilbert Selective Scan mechanism.<n>This mechanism explores the feature space more effectively, capturing intricate fine-scale details and improving overall coverage.<n>We believe that this refined strategy not only advances the state-of-the-art in low-light image enhancement but also holds promise for broader applications in fields that leverage Mamba-based techniques.
• Score: 2.9138744171708115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose an innovative enhancement to the Mamba framework by increasing the Hausdorff dimension of its scanning pattern through a novel Hilbert Selective Scan mechanism. This mechanism explores the feature space more effectively, capturing intricate fine-scale details and improving overall coverage. As a result, it mitigates information inconsistencies while refining spatial locality to better capture subtle local interactions without sacrificing the model's ability to handle long-range dependencies. Extensive experiments on publicly available benchmarks demonstrate that our approach significantly improves both the quantitative metrics and qualitative visual fidelity of existing Mamba-based low-light image enhancement methods, all while reducing computational resource consumption and shortening inference time. We believe that this refined strategy not only advances the state-of-the-art in low-light image enhancement but also holds promise for broader applications in fields that leverage Mamba-based techniques.

Related papers

• MirrorMamba: Towards Scalable and Robust Mirror Detection in Videos [64.87702843502889]
  We propose a new effective and scalable video mirror detection method, called MirrorMamba.<n>Our approach leverages multiple cues to adapt to diverse conditions, incorporating perceived depth, correspondence and optical.<n> Notably, this work marks the first successful application of the Mamba-based architecture in the field of mirror detection.
  arXiv  Detail & Related papers  (2025-11-10T05:18:14Z)
• EAMamba: Efficient All-Around Vision State Space Model for Image Restoration [11.190025966582041]
  This study introduces Efficient All-Around Mamba (EAMamba), an enhanced framework that incorporates a Multi-Head Selective Scan Module (MHSSM) with an all-around scanning mechanism.<n>EAMamba achieves a significant 31-89% reduction in FLOPs while maintaining favorable performance compared to existing low-level Vision Mamba methods.
  arXiv  Detail & Related papers  (2025-06-27T14:12:58Z)
• OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model [8.370356454348554]
  We propose OSDMamba, the first Mamba-based architecture specifically designed for oil spill detection.<n>We show that the proposed OSDMamba achieves state-of-the-art performance, yielding improvements of 8.9% and 11.8% in OSD across two publicly available datasets.
  arXiv  Detail & Related papers  (2025-06-22T12:07:44Z)
• MambaStyle: Efficient StyleGAN Inversion for Real Image Editing with State-Space Models [60.110274007388135]
  MambaStyle is an efficient single-stage encoder-based approach for GAN inversion and editing.<n>We show that MambaStyle achieves a superior balance among inversion accuracy, editing quality, and computational efficiency.
  arXiv  Detail & Related papers  (2025-05-06T20:03:47Z)
• ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model [51.83639270669481]
  Unsupervised anomaly detection in hyperspectral images (HSI) aims to detect unknown targets from backgrounds.<n>HSI studies are hindered by steep computational costs due to the high-dimensional property of HSI and dense sampling-based training paradigm.<n>We propose an Asymmetrical Consensus State Space Model (ACMamba) to significantly reduce computational costs without compromising accuracy.
  arXiv  Detail & Related papers  (2025-04-16T05:33:42Z)
• DefMamba: Deformable Visual State Space Model [65.50381013020248]
  We propose a novel visual foundation model called DefMamba.<n>By combining a deformable scanning(DS) strategy, this model significantly improves its ability to learn image structures and detects changes in object details.<n>Numerous experiments have shown that DefMamba achieves state-of-the-art performance in various visual tasks.
  arXiv  Detail & Related papers  (2025-04-08T08:22:54Z)
• MambaIC: State Space Models for High-Performance Learned Image Compression [40.155314987485376]
  A high-performance image compression algorithm is crucial for real-time information transmission across numerous fields.<n>Inspired by the effectiveness of state space models (SSMs) in capturing long-range dependencies, we leverage SSMs to address computational inefficiency in existing methods.<n>We propose an enhanced image compression approach through refined context modeling, which we term MambaIC.
  arXiv  Detail & Related papers  (2025-03-16T11:32:34Z)
• Detail Matters: Mamba-Inspired Joint Unfolding Network for Snapshot Spectral Compressive Imaging [40.80197280147993]
  We propose a Mamba-inspired Joint Unfolding Network (MiJUN) to overcome the inherent nonlinear and ill-posed characteristics of HSI reconstruction.<n>We introduce an accelerated unfolding network scheme, which reduces the reliance on initial optimization stages.<n>We refine the scanning strategy with Mamba by integrating the tensor mode-$k$ unfolding into the Mamba network.
  arXiv  Detail & Related papers  (2025-01-02T13:56:23Z)
• Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer Aggregation [4.227991281224256]
  This paper proposes to fully utilize complementary advantages from Mamba and Transformer without sacrificing computation efficiency.<n>The selective scanning mechanism of Mamba is employed to focus on spatial modeling, enabling capture long-range spatial dependencies.<n>The self-attention mechanism of Transformer is applied to focus on channel modeling, avoiding high burdens that are in quadratic growth with image's spatial dimensions.
  arXiv  Detail & Related papers  (2024-12-20T12:36:34Z)
• Coherent and Multi-modality Image Inpainting via Latent Space Optimization [61.99406669027195]
  PILOT (intextbfPainting vtextbfIa textbfLatent textbfOptextbfTimization) is an optimization approach grounded on a novel textitsemantic centralization and textitbackground preservation loss. Our method searches latent spaces capable of generating inpainted regions that exhibit high fidelity to user-provided prompts while maintaining coherence with the background.
  arXiv  Detail & Related papers  (2024-07-10T19:58:04Z)
• LLEMamba: Low-Light Enhancement via Relighting-Guided Mamba with Deep Unfolding Network [9.987504237289832]
  We propose a novel Low-Light Enhancement method via relighting-guided Mamba with a deep unfolding network (LLEMamba) Our LLEMamba first constructs a Retinex model with deep priors, embedding the iterative optimization process based on the Alternating Direction Method of Multipliers (ADMM) within a deep unfolding network. Unlike Transformer, to assist the deep unfolding framework with multiple iterations, the proposed LLEMamba introduces a novel Mamba architecture with lower computational complexity.
  arXiv  Detail & Related papers  (2024-06-03T06:23:28Z)
• Local Magnification for Data and Feature Augmentation [53.04028225837681]
  We propose an easy-to-implement and model-free data augmentation method called Local Magnification (LOMA) LOMA generates additional training data by randomly magnifying a local area of the image. Experiments show that our proposed LOMA, though straightforward, can be combined with standard data augmentation to significantly improve the performance on image classification and object detection.
  arXiv  Detail & Related papers  (2022-11-15T02:51:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.