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

Larger Hausdorff Dimension in Scanning Pattern Facilitates Mamba-Based Methods in Low-Light Image Enhancement | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 December 2025 V1 Latest version Share on Larger Hausdorff Dimension in Scanning Pattern Facilitates Mamba-Based Methods in Low-Light Image Enhancement Authors : Xinhua Wang 0009-0001-8530-8885 [email protected] , Caibo Feng , Xiangjun Fu , and Chunxiao Liu Authors Info & Affiliations https://doi.org/10.22541/au.176463780.06196508/v1 110 views 113 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In the domain of low-light image enhancement, both transformerbased approaches, such as Retinexformer and Mamba-based frameworks, such as MambaLLIE, have demonstrated distinct advantages alongside inherent limitations. Transformer-based methods, in comparison with mamba-based methods, can capture local interactions more effectively, albeit often at a high computational cost. In contrast, Mamba-based techniques provide efficient global information modeling with linear complexity, yet they encounter two significant challenges: (1) inconsistent feature representation at the margins of each scanning row and (2) insufficient capture of fine-grained local interactions. To overcome these challenges, 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. Supplementary Material File (manuscript2.pdf) Download 8.40 MB Information & Authors Information Version history V1 Version 1 02 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords hausdorff dimension hilbert scan low-light image enhancement mamba-based methods scanning pattern Authors Affiliations Xinhua Wang 0009-0001-8530-8885 [email protected] Imperial College London London View all articles by this author Caibo Feng University of Sussex View all articles by this author Xiangjun Fu University of California View all articles by this author Chunxiao Liu Zhejiang Gongshang University View all articles by this author Metrics & Citations Metrics Article Usage 110 views 113 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xinhua Wang, Caibo Feng, Xiangjun Fu, et al. Larger Hausdorff Dimension in Scanning Pattern Facilitates Mamba-Based Methods in Low-Light Image Enhancement. Authorea . 02 December 2025. DOI: https://doi.org/10.22541/au.176463780.06196508/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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