Learning to Robustly Reconstruct Low-light Dynamic Scenes from Spike
  Streams
Liwen Hu Ziluo Ding Mianzhi Liu Lei Ma Tiejun Huang
As a neuromorphic sensor with high temporal resolution, spike camera can
generate continuous binary spike streams to capture per-pixel light intensity.
We can use reconstruction methods to restore scene details in high-speed
scenarios. However, due to limited information in spike streams, low-light
scenes are difficult to effectively reconstruct. In this paper, we propose a
bidirectional recurrent-based reconstruction framework, including a
Light-Robust Representation (LR-Rep) and a fusion module, to better handle such
extreme conditions. LR-Rep is designed to aggregate temporal information in
spike streams, and a fusion module is utilized to extract temporal features.
Additionally, we have developed a reconstruction benchmark for high-speed
low-light scenes. Light sources in the scenes are carefully aligned to
real-world conditions. Experimental results demonstrate the superiority of our
method, which also generalizes well to real spike streams. Related codes and
proposed datasets will be released after publication.