<p>Images captured under low-light conditions are characterized by poor quality and insufficient exposure, which adversely affects the performance of downstream tasks, such as autonomous driving and nighttime surveillance. Recently, Transformer-based methods have achieved notable success in low-light image enhancement. However, these methods exhibit limited local information modeling capabilities and encounter issues with outliers due to insufficient dynamic range, which curtail their performance in low-light image enhancement. Additionally, the quadratic computational complexity of their Softmax-based self-attention mechanisms renders these methods challenging to deploy on edge devices. To address these issues, we propose a memristor-based Bi-Path Wavelet Transformer (BWT) with linear computational complexity. Specifically, we design a novel Dual-path Wavelet Linear Attention (BWLA) to replace the Softmax-based self-attention, enabling efficient local and global information extraction and aggregation at linear complexity. We propose a hardware implementation scheme of BWT based on memristors, which reduces deployment complexity and offers an effective solution for deploying low-light enhancement algorithms on edge devices. Experiments on multiple low-light enhancement benchmark datasets demonstrate that our method outperforms multiple state-of-the-art (SOTA) methods.</p>