论文标题
图像稀疏表示和动态视觉传感器数据压缩的尖峰采样网络
Spiking sampling network for image sparse representation and dynamic vision sensor data compression
论文作者
论文摘要
稀疏表示引起了极大的关注，因为它可以大大节省存储资源并在低维空间中找到数据的代表性特征。结果，它可以广泛应用于工程领域，包括功能提取，压缩感应，信号降解，图片群集和词典学习，仅举几例。在本文中，我们提出了一个尖峰采样网络。该网络由尖峰神经元组成，它可以动态地决定应保留哪些像素点，并且需要根据输入掩盖哪些点。我们的实验表明，与随机抽样相比，这种方法可以更好地稀疏表示原始图像，并促进图像重建。因此，我们使用这种方法来压缩来自动态视觉传感器的大量数据，从而大大降低了事件数据的存储要求。
Sparse representation has attracted great attention because it can greatly save storage resources and find representative features of data in a low-dimensional space. As a result, it may be widely applied in engineering domains 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 spiking neurons, and it can dynamically decide which pixel points should be retained and which ones need to be masked according to the input. Our experiments demonstrate that this approach enables better sparse representation of the original image and facilitates image reconstruction compared to random sampling. We thus use this approach for compressing massive data from the dynamic vision sensor, which greatly reduces the storage requirements for event data.
