2018/11/26 by Liyuan Pan, Pan, Liyuan, Cedric Scheerlinck +9 · 19 citations
Computer Science · Engineering · Medicine · Neuroscience · #Advanced MRI Techniques and Applications #Advanced Memory and Neural Computing #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Neural dynamics and brain function #cs.CV
paper · pdf · doi:10.48550/arxiv.1811.10180
14 pages, 11 figures
openalex publication_date 2018/11/26 · arxiv created 2018/11/27 · arxiv updated 2018/11/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Event-based cameras can measure intensity changes (called `\it events') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the active pixel sensor (APS), the event camera allows simultaneous output of the intensity frames. However, the output images are captured at a relatively low frame-rate and often suffer from motion blur. A blurry image can be regarded as the integral of a sequence of latent images, while the events indicate the changes between the latent images. Therefore, we are able to model the blur-generation process by associating event data to a latent image. In this paper, we propose a simple and effective approach, the Event-based Double Integral (EDI) model, to reconstruct a high frame-rate, sharp video from a single blurry frame and its event data. The video generation is based on solving a simple non-convex optimization problem in a single scalar variable. Experimental results on both synthetic and real images demonstrate the superiority of our EDI model and optimization method in comparison to the state-of-the-art.