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• W4312273313 abstract "This paper proposes to use neuromorphic events for correcting rolling shutter (RS) images as consecutive global shutter (GS) frames. RS effect introduces edge distortion and region occlusion into images caused by row-wise read-out of CMOS sensors. We introduce a novel computational imaging setup consisting of an RS sensor and an event sensor, and propose a neural network called EvUnroll to solve this problem by exploring the high-temporal-resolution property of events. We use events to bridge a spatio-temporal connection between RS and GS, establish a flow estimation module to correct edge distortions, and design a synthesis-based restoration module to restore occluded regions. The results of two branches are fused through a refining module to generate corrected GS images. We further propose datasets captured by a high-speed camera and an RS-Event hybrid camera system for training and testing our network. Experimental results on both public and proposed datasets show a systematic performance improvement compared to state-of-the-art methods." @default.
• W4312273313 created "2023-01-04" @default.
• W4312273313 creator A5029760958 @default.
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• W4312273313 date "2022-06-01" @default.
• W4312273313 modified "2023-09-26" @default.
• W4312273313 title "EvUnroll: Neuromorphic Events based Rolling Shutter Image Correction" @default.
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• W4312273313 doi "https://doi.org/10.1109/cvpr52688.2022.01725" @default.
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