  Spike camera mimicking the retina fovea can report per-pixel luminance
intensity accumulation by firing spikes. As a bio-inspired vision sensor with
high temporal resolution, it has a huge potential for computer vision. However,
the sampling model in current Spike camera is so susceptible to quantization
and noise that it cannot capture the texture details of objects effectively. In
this work, a robust visual sampling model inspired by receptive field (RVSM) is
proposed where wavelet filter generated by difference of Gaussian (DoG) and
Gaussian filter are used to simulate receptive field. Using corresponding
method similar to inverse wavelet transform, spike data from RVSM can be
converted into images. To test the performance, we also propose a high-speed
motion spike dataset (HMD) including a variety of motion scenes. By comparing
reconstructed images in HMD, we find RVSM can improve the ability of capturing
information of Spike camera greatly. More importantly, due to mimicking
receptive field mechanism to collect regional information, RVSM can filter high
intensity noise effectively and improves the problem that Spike camera is
sensitive to noise largely. Besides, due to the strong generalization of
sampling structure, RVSM is also suitable for other neuromorphic vision sensor.
Above experiments are finished in a Spike camera simulator.
