Abstract: We present a novel computational imaging system with high resolution and low noise. Our system consists of a traditional video camera which captures high-resolution intensity images, and an event camera which encodes high-speed motion as a stream of asynchronous binary events. To process the hybrid input, we propose a unifying framework that ﬁrst bridges the two sensing modalities via a noise-robust motion compensation model, and then performs joint image ﬁltering. The ﬁltered output represents the temporal gradient of the captured space-time volume, which can be viewed as motion-compensated event frames with high resolution and low noise. Therefore, the output can be widely applied to many existing event-based algorithms that are highly dependent on spatial resolution and noise robustness. In experimental results performed on both publicly available datasets as well as our contributing RGB-DAVIS dataset, we show systematic performance improvement in applications such as high frame-rate video synthesis, feature/corner detection and tracking, as well as high dynamic range image reconstruction.
Citation: Zihao W. Wang, Peiqi Duan, Oliver Cossairt, Aggelos Katsaggelos, Tiejun Huang, Boxin Shi, "Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging," the IEEE Conference on Computer Vision and Pattern Recognition Seattle, June (2020)