Joint Filtering Of Intensity Images And Neuromorphic Events For High-Resolution Noise-Robust Imaging
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)(2020)
摘要
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 new 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.
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关键词
motion-compensated event frames,event-based algorithms,spatial resolution,high frame-rate video synthesis,high dynamic range image reconstruction,neuromorphic events,high-resolution noise-robust imaging,computational imaging system,video camera,high-resolution intensity images,event camera,asynchronous binary events,noise-robust motion compensation model,joint image filtering,RGB-DAVIS dataset
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