Spike-RetinexFormer: Rethinking Low-light Image Enhancement with Spiking Neural Networks
Hongzhi Wang, Xiubo Liang, Jinxing Han, Weidong Geng
Low-light image enhancement (LLIE) aims to improve the visibility and quality of images captured under poor illumination. However, existing deep enhancement methods often neglect computational efficiency, resulting in high energy and memory costs. We propose \textbf{Spike-RetinexFormer}, a novel LLIE architecture that synergistically combines Retinex theory with spiking neural networks(SNNs) and Transformer-based design. By leveraging sparse spike-based computations, Spike-RetinexFormer drastically reduces energy consumption and memory usage compared to conventional approaches. Extensive evaluations across thirteen diverse LLIE benchmarks demonstrate that Spike-RetinexFormer achieves state-of-the-art performance, often improving PSNR by 0.5–1.5 dB over previous best methods (attaining 25.50 dB on LOL-v1 and 30.37 dB on SDSD-out) while using significantly less power. Our work pioneers the synergistic integration of SNNs within Transformer architectures for LLIE, establishing a compelling pathway towards powerful, energy-efficient low-level vision on resource-constrained platforms.