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- W3215657816 abstract "Spike camera is a new type of bio-inspired vision sensor, each pixel of which perceives the brightness of the scene independently, and finally outputs 3-dimensional spatiotemporal spike streams. To bridge the spike camera and traditional frame-based vision, there is some works to reconstruct spike streams into regular images. However, the low spatial resolution ( <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$400times 250$ </tex-math></inline-formula> ) of the spike camera limits the quality of the reconstructed images. Thus, it is meaningful to explore a super-resolution reconstruction for spike streams. In this paper, we propose an end-to-end network to reconstruct high-resolution images from low-resolution spike streams. To utilize more spatiotemporal features of spike streams, our network adopts a multi-level features learning mechanism, including intra-stream feature extraction by spike encoder, inter-stream dependencies extraction based on optical flow module, and joint features learning via spike-based iterative projection. Experimental results demonstrate that our network is superior to the combination of state-of-the-art intensity image reconstruction methods and super-resolution networks on simulated and real datasets." @default.
- W3215657816 created "2021-12-06" @default.
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- W3215657816 date "2023-01-01" @default.
- W3215657816 modified "2023-10-18" @default.
- W3215657816 title "Learning Super-Resolution Reconstruction for High Temporal Resolution Spike Stream" @default.
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- W3215657816 doi "https://doi.org/10.1109/tcsvt.2021.3130147" @default.
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