  Conventional frame-based camera is not able to meet the demand of rapid
reaction for real-time applications, while the emerging dynamic vision sensor
(DVS) can realize high speed capturing for moving objects. However, to achieve
visual texture reconstruction, DVS need extra information apart from the output
spikes. This paper introduces a fovea-like sampling method inspired by the
neuron signal processing in retina, which aims at visual texture reconstruction
only taking advantage of the properties of spikes. In the proposed method, the
pixels independently respond to the luminance changes with temporal
asynchronous spikes. Analyzing the arrivals of spikes makes it possible to
restore the luminance information, enabling reconstructing the natural scene
for visualization. Three decoding methods of spike stream for texture
reconstruction are proposed for high-speed motion and stationary scenes.
Compared to conventional frame-based camera and DVS, our model can achieve
better image quality and higher flexibility, which is capable of changing the
way that demanding machine vision applications are built.
