High-Speed Scene Reconstruction from Low-Light Spike Streams

Benefiting from the high temporal resolution, the spike camera shows promising potential in capturing high-speed scenes via accumulating luminance intensity and firing spikes. Although the spike camera compared to the high-speed camera is quite cost-effective, its performance in capturing low-light scenes is poor. Specifically, it takes more time for the spike camera to accumulate enough light signal for firing a spike in low-light scenes, while the scenes may have already changed because of the high-speed motion. There may be no effective spikes for a long time due to the insufficient incident light, and the signal-to-noise ratio of spike streams in low-light scenes is unsatisfactory. Thus, it's easy to introduce noise and motion blur while reconstructing, especially in rapidly changing scenes. To address this issue, we propose a low-light scene reconstruction method for the spike camera. In particular, we first develop a Brightness-Adaptive Light Inference (BALI) method to preliminarily reconstruct the low-light scene according to the brightness, which utilizes both the spike interval and the spike number. Considering the motion, we then estimate optical flow and filter the preliminary restored frames iteratively to handle the noise via temporal correlation. After that, there is still some noise, and we further handle it by a spatial filter according to the brightness. As a result, we restore a clear image based on both temporal and spatial correlation. The experimental results demonstrate that our method achieves good visual quality in low-light scene reconstruction.