DS-LIF: Novel Spiking Neuron for Reliable Low-Light Image Enhancement

The event-triggered mechanism and rich dynamic characteristics of spiking neural networks (SNNs) are highly compatible with the dynamic adaptive repair requirements of low-level visual restoration tasks. However, SNNs adopted for low-light image enhancement (LLIE) fail to accurately distinguish variations in structural information within under-illuminated regions and produce ambiguous spiking responses triggered by image edges, textures, and noise. To address this issue, we propose a reliability-aware spiking neuron named Dynamic-Sleep Leaky Integrate-and-Fire (DS-LIF). It scales the input current prior to membrane potential integration according to local illumination and color contrast, and dynamically masks weakly effective feature channels for image restoration. Subsequently, it combines the mean spike response across all time steps with the local noise, chromatic stability, and underexposure level of the generated image to produce a reliability-guided map. Based on DS-LIF, we construct a dual-stream HVI enhancement framework, SpikeRelight, which can effectively improve the quality of LLIE. To better facilitate cross-stream information interaction, we design a Reliability-Guided Dual-Frequency Interaction (RGDFI) module in this architecture, guided by the reliability-guided map. It decomposes features in the illumination flow and chrominance flow into low-frequency and high-frequency parts, and performs asymmetric cross-stream interaction. Extensive experiments on both paired and unpaired benchmarks, along with ablation studies, demonstrate that SpikeRelight achieves competitive performance.