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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2020.3036667
|2 doi
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|a pubmed24n1057.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Pan, Liyuan
|e verfasserin
|4 aut
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| 245 |
1 |
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|a High Frame Rate Video Reconstruction Based on an Event Camera
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 04.04.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Event-based cameras measure intensity changes (called 'events') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the 'active pixel sensor' (APS), the 'Dynamic and Active-pixel Vision Sensor' (DAVIS) allows the simultaneous output of intensity frames and events. However, the output images are captured at a relatively low frame rate and often suffer from motion blur. A blurred image can be regarded as the integral of a sequence of latent images, while events indicate changes between the latent images. Thus, we are able to model the blur-generation process by associating event data to a latent sharp image. Based on the abundant event data alongside a low frame rate, easily blurred images, we propose a simple yet effective approach to reconstruct high-quality and high frame rate sharp videos. Starting with a single blurred frame and its event data from DAVIS, we propose the Event-based Double Integral (EDI) model and solve it by adding regularization terms. Then, we extend it to multiple Event-based Double Integral (mEDI) model to get more smooth results based on multiple images and their events. Furthermore, we provide a new and more efficient solver to minimize the proposed energy model. By optimizing the energy function, we achieve significant improvements in removing blur and the reconstruction of a high temporal resolution video. 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 datasets demonstrate the superiority of our mEDI model and optimization method compared to the state-of-the-art
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|a Journal Article
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| 700 |
1 |
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|a Hartley, Richard
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Scheerlinck, Cedric
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Miaomiao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yu, Xin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Dai, Yuchao
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 5 vom: 14. Mai, Seite 2519-2533
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:44
|g year:2022
|g number:5
|g day:14
|g month:05
|g pages:2519-2533
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2020.3036667
|3 Volltext
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