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 cameras 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 introduce fluctuations to spikes, making the recorded spike intervals unstable and unable to reflect accurate light intensities. 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 unbiased 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 orders 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 demonstrate the effectiveness of our method on both synthetic and real-captured data. The source code and dataset are available at https://github.com/ruizhao26/BSF.