Denoising algorithm for space target event streams based on event camera

  Objective   Event camera can capture the real-time changes of the scene. It only outputs brightness changes of the pixel level and asynchronous event stream with microsecond resolution. It has the advantages of high event resolution, high dynamic range, low delay and low bandwidth. Its application in the space target detection has gradually attracted the attention of researchers. At present, there are following challenges in the application of event camera. On the one hand, event camera is sensitive to environmental changes and outputs a lot of noise. On the other hand, the remote detection of space target will output the point-target event stream, resulting in a low signal-to-noise ratio, which demands higher requirements for the processing algorithm of space event stream. Therefore, denoising algorithm for space target event streams is very important for data preprocessing. For this purpose, denoising algorithm based on event camera is proposed.

  Methods   For event stream data of space targets, this paper proposes the Neighborhood Density-based Spatiotemporal Event Filter (NDSEF), which is based on the neighborhood density, to reduce the local spatial neighborhood noise of each time neighborhood by compressing the image frame. Combined with the characteristics of space target trajectory, a circular local sliding window is set to adjust the selection range of spatial neighborhood, and noise filtering based on spatial information is realized (Fig.3). On this basis, this paper proposes a cascade filter based on NDSEF for different scenes and targets in the space environment. Through multiple stages of increasing the cumulative window of pixel dimensions, the multi-dimensional combination filter can gradually refine the event data and obtain the best noise reduction performance.

  Results and Discussions   This paper demonstrates the high-speed and high-generalization ability of the denoising algorithm in the public datasets and the simulation datasets. The scene information of the experimental datasets is shown (Tab.1-2), including three single-target scenes, a double-targets scene and a simulated space scene. The proposed filter outperforms the classical filter in signal-to-noise ratio and noise ratio (Fig.6, Tab.3), and the event processing speed can reach 10 μs, which meets the requirement of real-time detection of the space targets. Meanwhile, noise events can be effectively processed for multi-target event streams (Fig.5, Fig.7). The experimental results show that the proposed filter can ensure the accuracy and processing speed of the denoising algorithm in the space scene with low SNR.

  Conclusions   This paper introduces denoising algorithm for space target event streams based on event camera, namely NDSEF algorithm, which makes full use of spatio-temporal constraints and signal characteristics of low signal-to-noise ratio. By compressing image frames, local spatial neighborhood denoising is processed for each time neighborhood. By combining space target trajectory characteristics, circular local sliding window is set to adjust the selection range of space neighborhood. On this basis, the cascaded filter based on NDSEF is proposed to increase the accumulation of pixel dimension windows to achieve a high degree of the algorithm optimization. The experimental results show that the proposed filter has obvious effect on denoising, the target signal is clearly visible. The signal-to-noise ratio and noise ratio are significantly improved, and the event processing speed is up to 10 μs. For space multi-target event streams under extreme conditions, it has the advantages of accuracy and real-time, which lays the foundation for the space multi-target detection based on event camera.