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• W3097097310 abstract "Event-based sensors, which have a response if the change of pixel intensity exceeds a triggering threshold, can capture high-speed motion with microsecond accuracy. Assisted by an event camera, we can generate high frame-rate sharp videos from low frame-rate blurry ones captured by an intensity camera. In this paper, we propose an effective event-driven video deblurring and interpolation algorithm based on deep convolutional neural networks (CNNs). Motivated by the physical model that the residuals between a blurry image and sharp frames are the integrals of events, the proposed network uses events to estimate the residuals for the sharp frame restoration. As the triggering threshold varies spatially, we develop an effective method to estimate dynamic filters to solve this problem. To utilize the temporal information, the sharp frames restored from the previous blurry frame are also considered. The proposed algorithm achieves superior performance against state-of-the-art methods on both synthetic and real datasets." @default.
• W3097097310 created "2020-11-09" @default.
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• W3097097310 date "2020-01-01" @default.
• W3097097310 modified "2023-09-24" @default.
• W3097097310 title "Learning Event-Driven Video Deblurring and Interpolation" @default.
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• W3097097310 doi "https://doi.org/10.1007/978-3-030-58598-3_41" @default.
• W3097097310 hasPublicationYear "2020" @default.
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