Learning to Trust Generative Features for Spike Image Reconstruction

Spike cameras encode dynamic scenes as binary firing streams with ultra-high temporal resolution and high dynamic range. Recovering fine details from spike sequences remains challenging for reconstruction models based on direct regression. Generative restoration can provide complementary information, but its reliability varies across image regions. We propose LTGF, a framework that learns to trust generative features to enhance a base regression network. Conditioned on base spike features, a shared flow matching network starts from coarse intensity estimates based on spike firing rates (TFP) and inter-spike intervals (TFI) to generate complementary candidates. To select useful candidate information, multi-level uncertainty estimates in log space are combined with a learned content-adaptive tolerance that accounts for reconstruction benefit. The resulting weights control residual feature corrections while retaining the base representation. Experiments demonstrate the best performance among the compared methods on synthetic spike data and real spike data.