BSMamba: Brightness and Semantic Modeling for Long-Range Interaction in Low-Light Image Enhancement

TL;DR

BSMamba introduces a novel architecture for low-light image enhancement that leverages brightness and semantic-guided attention to improve long-range interactions, resulting in state-of-the-art performance and better semantic preservation.

Contribution

The paper proposes BSMamba, a new visual Mamba architecture with brightness and semantic components, enabling more effective long-range token interactions in low-light image enhancement.

Findings

Achieves state-of-the-art results in LLIE tasks.

Effectively preserves semantic consistency during enhancement.

Outperforms existing methods in brightness restoration.

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. Code is available at https://github.com/bywlzts/BSMamba.