Spk2ImgMoE: Heterogeneous Mixture-of-Experts for High-Fidelity Spike-to-Image Reconstruction

Spike-to-image reconstruction remains challenging due to the strong heterogeneity of spiking data across space, time, and noise conditions. Most existing reconstruction methods process all features with uniform computation, which is inefficient and limits reconstruction quality. We propose Spk2ImgMoE, a Mixture-of-Experts-based framework for high-fidelity spike-to-image reconstruction. Spk2ImgMoE integrates Mixture-of-Experts at a mid-level semantic feature stage, enabling adaptive feature refinement with conditional computation. In particular, Spk2ImgMoE incorporates a Heterogeneous Mixture-of-Experts (HMoE) module that models the intrinsic heterogeneity of spiking data through structurally diverse experts with different computational capacities. In addition, a bias-driven routing mechanism introduces a learnable bias into the gating logits, serving as a dynamic load balancer that regulates expert utilization and prevents expert collapse without auxiliary loss constraints. Extensive experiments on multiple datasets show that Spk2ImgMoE improves reconstruction quality while maintaining a favorable balance between performance and computational efficiency, confirming the effectiveness of heterogeneous capacity allocation for spiking data.