Images captured in real-world low-light environments often exhibit spatially heterogeneous exposure, where uneven luminance disparities degrade perceptual quality and hinder the performance of downstream vision algorithms Tohave a different preference regarding the publication of your mail address(s) please indicate this clearly. If no changes are required address these challenges, we introduce a novel adaptive brightness–texture enhancement framework that dynamically reconciles the trade-off between local detail restoration and global illumination consistency. Specifically, the proposed pipeline partitions the input into non-overlapping patches and embeds them into a high-dimensional feature space, after which a cascade of Brightness–Texture Guide Mamba Modules and a subsequent Global Refine Module collaboratively optimize fine-grained textures and overall exposure. Moreover, we design a dedicated Local Restoration Module alongside a Brightness–Texture State-Space Model to leverage both brightness- and texture-order sequencing to facilitate efficient long-range interactions that preserve structural and brightness fidelity across the entire image. Experimental evaluations on multiple benchmark datasets demonstrate that our approach surpasses existing state-of-the-art methods across majority of objective metrics and in terms of subjective visual quality, notably by preserving fine details while maintaining global coherence.