## 1 Introduction  
Typical emerging vision applications such as autonomous driving [1], unmanned aerial vehicles [2], and wearable computing [3] require rapid reaction in computer vision processing [4]. When performing image analysis tasks such as detecting or tracking an object, most conventional cameras exploit a fixed low sampling rate and compress the scenes in the exposure time into one frame. These consecutive frames have to be compared to recover temporal changes [5], which may suffer from serious motion blur in high-speed scenarios and are computationally expensive [6]. Different from conventional frame-based cameras, bio-inspired cameras have been proposed to record high-speed motion. Event-based cameras, such as Dynamic Vision Sensors (DVS) [7; 8; 9; 10] and Dynamic and Active pixel Vision Sensors (DAVIS) [11], can acquire a stream of asynchronous events based on the variation of light intensity for independent pixels. However, event-based cameras are not able to acquire visual images as the conventional camera does since they only record the change information. Although there are some hybrid sensors combining DVS with a frame-based camera [12; 13], a conventional image sensor [14], or a photo-measurement circuit [15] to reconstruct textures, mismatch still exists due to the difference in the sampling rate and time, which hinders the performance of the reconstruction. Addressing the issue of capturing high-speed visual texture while recording the continuous-time signal, a retina-inspired spike camera with high spatial (\(250\times 400\)) and temporal resolutions (40,000 Hz) has been proposed [16; 17]. Unlike traditional digital cameras that record the visual information of the entire exposure process as a snapshot, a spike camera abandons the concept of exposure window. Instead, it continuously monitors the intensity of incoming light, with each pixel firing a stream of spikes that independently record the intensity of light over time. The emission of each spike represents the arrival of a very small number of photons, and the spike stream is recorded with very high temporal resolution, which allows high-speed motion scenes to be recovered from the spike sequence [18; 19; 20; 21]. However, reconstructing visual images from the spikes remains a challenge. Previous reconstruction algorithms suffer from low contrast or blur. A typical method, TFI (Texture from ISI) [20], uses two neighboring spikes to measure the instantaneous brightness intensity, which can reconstruct the outline of the texture for high-speed motion well but may generate undesirable noise in stationary scene reconstruction. Other methods, such as TFP (Texture from Playback) [18] and TFA (Texture from Adaptive threshold) [20], average the spikes over a large moving time window in a specific period to improve the signal-to-noise ratio (SNR), but the reconstructions could suffer unexpected motion blur, especially in a high-speed scene. Moreover, the TFP method with a small moving window utilized for dynamic area reconstruction may introduce unexpected noise. The signal-to-noise ratio is improved by motion aligned filtering [22] via utilizing temporal correlations of signals, but it can only be applied to scenes with linear motion. Spk2 ImgNet [23] can reconstruct high-quality images for dynamic scenes by using a deep convolutional neural network but costs huge computational complexity. To construct the high-speed motion and stationary scenes, methods, such as the SNM three-layer spiking neural model [24] and TFSTP (texture from short-term plasticity) [25], are proposed to distinguish the spike states (dynamic or static) in an incremental way, which are effective in reconstructing visual images in both stationary and high-speed scenes. After continuous-time-spikes processing by a combination of biologically plausible mechanisms, however, the TFA-like SNM method is utilized for the reconstruction according to the state of the neuron and the firing threshold, which may still suffer the problem of motion blur.