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- W4328137219 endingPage "119917" @default.
- W4328137219 startingPage "119917" @default.
- W4328137219 abstract "Event-based video deblurring is a method that performs deblurring by taking the event sequence data obtained from an event camera, which is composed of bio-inspired sensors, along with blurry frames as input. Event-based video deblurring has gained attention as a method that can overcome the limitations of conventional frame-based video deblurring. In this study, we propose a novel event-based video deblurring network based on convolution neural networks (CNNs). Unlike the existing event-based deblurring methods that only use event data, the proposed method fuses all the available information from current blurry frames, previously recovered sharp frames, and event data to deblur a video. Specifically, we propose an image and event feature fusion (IEFF) module to fuse event data with current intensity frame information. Additionally, we propose a current-frame reconstruction from previous-frame (CRP) module for acquiring a pseudo sharp frame from a previously recovered sharp frame and a fusion-based residual estimation (FRE) module, which fuses the event features with the image features of the previous sharp frame extracted from the CRP module. We demonstrate through a verification experiment using synthetic and real datasets that the proposed method has superior quantitative and qualitative results compared to state-of-the-art methods." @default.
- W4328137219 created "2023-03-22" @default.
- W4328137219 creator A5019773104 @default.
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- W4328137219 date "2023-08-01" @default.
- W4328137219 modified "2023-09-25" @default.
- W4328137219 title "Event-based video deblurring based on image and event feature fusion" @default.
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- W4328137219 doi "https://doi.org/10.1016/j.eswa.2023.119917" @default.
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