SpikeInpaint: Hybrid ANN–SNN Image Inpainting with Ternary Spike Coding

Image inpainting is dominated by generators that perform dense floating-point computation throughout the network, incurring substantial computational and energy costs. We investigate whether sparse spiking computation can match the reconstruction quality of dense computation at a lower activation cost. We present SpikeInpaint, a hybrid ANN-SNN generator that places spiking computation only where the spatial resolution is lowest: the four FFC blocks of the bottleneck and the two channel-transition modules on either side of it. The bottleneck combines ternary hard-reset spiking neurons with channel compression, and a channel-wise gate in the decoder fuses the ANN and SNN paths per channel. Experiments on Places2 and CelebA-HQ show that SpikeInpaint achieves 95.89% spike sparsity during inference, within 0.09 dB of an otherwise identical all-ANN generator on Places2. These results show that sparse spiking computation can support dense image generation, extending SNNs to dense generative vision.