Abstract
Current low-light image enhancement (LLIE) methods face significant limitations in simultaneously improving brightness while preserving semantic consistency, fine details, and computational efficiency. With the emergence of state-space models, particularly Mamba, image restoration has achieved remarkable performance, yet existing visual Mamba approaches flatten 2D images into 1D token sequences using fixed scanning rules, critically limiting interactions between distant tokens with causal relationships and constraining their ability to capture meaningful long-range dependencies. To address these fundamental limitations, we propose BSMamba, a novel visual Mamba architecture comprising two specially designed components: Brightness Mamba and Semantic Mamba. The Brightness Mamba revolutionizes token interaction patterns by prioritizing connections between distant tokens with similar brightness levels, effectively addressing the challenge of brightness restoration in LLIE tasks through brightness-guided selective attention. Complementing this, the Semantic Mamba establishes priority interactions between tokens sharing similar semantic meanings, allowing the model to maintain contextual consistency by connecting semantically related regions across the image, thus preserving the hierarchical nature of image semantics during enhancement. By intelligently modeling tokens based on brightness and semantic similarity rather than arbitrary scanning patterns, BSMamba transcends the constraints of conventional token sequencing while adhering to the principles of causal modeling. Extensive experiments demonstrate that BSMamba achieves state-of-the-art performance in LLIE while preserving semantic consistency.
Abstract (translated)
目前的低光图像增强（LLIE）方法在同时提高亮度、保持语义一致性、精细细节和计算效率方面面临重大限制。随着状态空间模型，特别是Mamba模型的出现，图像恢复已取得了显著性能，然而现有的视觉Mamba方法将2D图像通过固定的扫描规则扁平化为1D标记序列，这极大地限制了具有因果关系的远距离标记之间的交互，并约束了它们捕捉有意义的长程依赖性的能力。为了克服这些基本限制，我们提出了一种新的视觉Mamba架构BSMamba，该架构包含两个特别设计的组件：亮度Mamba和语义Mamba。 亮度Mamba通过优先连接具有相似亮度级别的远距离标记来革新令牌交互模式，这有效地解决了LLIE任务中的亮度恢复挑战，通过基于亮度的选择性注意机制实现。此外，语义Mamba则建立了共享类似语义含义的标记之间的优先交互，允许模型通过链接图像中相关区域来保持上下文一致性，从而在增强过程中保留图像语义的层次结构特性。 BSMamba通过根据亮度和语义相似性而非随意扫描模式智能地建模令牌，超越了传统令牌排序的约束，同时遵循因果建模的原则。广泛的实验表明，BSMamba在低光图像增强方面取得了最先进的性能，并且能够保持语义一致性。
URL
https://arxiv.org/abs/2506.18346
