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Sparse representation has attracted great attention because it can greatlysave storage resources and find representative features of data in alow-dimensional space. As a result, it may be widely applied in engineeringdomains including feature extraction, compressed sensing, signal denoising,picture clustering, and dictionary learning, just to name a few. In this paper,we propose a spiking sampling network. This network is composed of spikingneurons, and it can dynamically decide which pixel points should be retainedand which ones need to be masked according to the input. Our experimentsdemonstrate that this approach enables better sparse representation of theoriginal image and facilitates image reconstruction compared to randomsampling. We thus use this approach for compressing massive data from thedynamic vision sensor, which greatly reduces the storage requirements for eventdata.

Spiking sampling network for image sparse representation and dynamic vision sensor data compression