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As a bio-inspired vision sensor with ultra-high speed, spike cameras exhibit great potential in recording dynamic scenes with high-speed motion or drastic light changes. Different from traditional cameras, each pixel in spike cam-eras records the arrival of photons continuously by firing binary spikes at an ultra-fine temporal granularity. In this process, multiple factors impact the imaging, including the photons' Poisson arrival, thermal noises from circuits, and quantization effects in spike readout. These factors intro-duce fluctuations to spikes, making the recorded spike in-tervals unstable and unable to reflect accurate light intensi-ties. In this paper, we present an approach to deal with spike fluctuations and boost spike camera image reconstruction. We first analyze the quantization effects and reveal the unbi-ased estimation attribute of the reciprocal of differential of spike firing time (DSFT). Based on this, we propose a spike representation module to use DSFT with multiple orders for fluctuation suppression, where DSFT with higher or-ders indicates spike integration duration between multiple spikes. We also propose a module for inter-moment feature alignment at multiple granularities. The coarser alignment is based on patch-level cross-attention with a local search strategy, and the finer alignment is based on deformable convolution at the pixel level. Experimental results demon-strate the effectiveness of our method on both synthetic and real-captured data. The source code and dataset are avail-able at https://github.com/ruizhao26/BSF.
Zhao et al. (Sun,) studied this question.