Recent advances in low light image enhancement have been dominated by Retinex-based learning framework, leveraging convolutional neural networks (CNNs) and Transformers. However, the vanilla Retinex theory primarily addresses global illumination degradation and neglects local issues such as noise and blur in dark conditions. Moreover, CNNs and Transformers struggle to capture global degradation due to their limited receptive fields. While state space models (SSMs) have shown promise in the long-sequence modeling, they face challenges in combining local invariants and global context in visual data. In this paper, we introduce MambaLLIE, an implicit Retinex-aware low light enhancer featuring a global-then-local state space design. We first propose a Local-Enhanced State Space Module (LESSM) that incorporates an augmented local bias within a 2D selective scan mechanism, enhancing the original SSMs by preserving local 2D dependency. Additionally, an Implicit Retinex-aware Selective Kernel module (IRSK) dynamically selects features using spatially-varying operations, adapting to varying inputs through an adaptive kernel selection process. Our Global-then-Local State Space Block (GLSSB) integrates LESSM and IRSK with LayerNorm as its core. This design enables MambaLLIE to achieve comprehensive global long-range modeling and flexible local feature aggregation. Extensive experiments demonstrate that MambaLLIE significantly outperforms state-of-the-art CNN and Transformer-based methods. Project Page: https://mamballie.github.io/anon/

在这篇论文中，我们引入了MambaLLIE，一种全局而后局部状态空间设计的隐式Retinex感知暗光增强器。我们首先提出了一种增强本地二维依赖的局部增强状态空间模块（LESSM），然后引入了一种通过自适应内核选择过程动态选择特征的隐式Retinex感知选择内核模块（IRSK）。我们的全局-局部状态空间块（GLSSB）将LESSM和IRSK与LayerNorm作为其核心进行整合。实验结果表明，MambaLLIE明显优于CNN和Transformer方法。

MambaLLIE: 全局-局部状态空间下的暗光增强技术与 Retinex 感知