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

TL;DR

This paper enhances the Mamba framework for low-light image enhancement by increasing the Hausdorff dimension of its scanning pattern using a Hilbert Selective Scan, leading to better detail capture and efficiency.

Contribution

Introduces a novel Hilbert Selective Scan mechanism that increases the Hausdorff dimension in Mamba-based methods, improving detail exploration and computational efficiency.

Findings

Significant improvement in quantitative metrics.

Enhanced visual fidelity in low-light images.

Reduced computational resource consumption.

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.