Fast event-inpainting based on lightweight generative adversarial nets
| العنوان: | Fast event-inpainting based on lightweight generative adversarial nets |
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| المؤلفون: | Kun Jin, Shengyue Huang, Huanran Ye, Sheng Liu, Haohao Cheng |
| المصدر: | Optoelectronics Letters. 17:507-512 |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Discriminator, Computer science, business.industry, Event (computing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Inpainting, Condensed Matter Physics, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Convolution, Time consistency, Computer vision, Artificial intelligence, Electrical and Electronic Engineering, business, Pose, Image resolution, Image restoration |
| الوصف: | Event-based cameras generate sparse event streams and capture high-speed motion information, however, as the time resolution increases, the spatial resolution will decrease sharply. Although the generative adversarial network has achieved remarkable results in traditional image restoration, directly using it for event inpainting will obscure the fast response characteristics of the event camera, and the sparsity of the event stream is not fully utilized. To tackle the challenges, an event-inpainting network is proposed. The number and structure of the network are redesigned to adapt to the sparsity of events, and the dimensionality of the convolution is increased to retain more spatiotemporal information. To ensure the time consistency of the inpainting image, an event sequence discriminator is added. The tests on the DHP19 and MVSEC datasets were performed. Compared with the state-of-the-art traditional image inpainting method, the method in this paper reduces the number of parameters by 93.5% and increases the inference speed by 6 times without reducing the quality of the restored image too much. In addition, the human pose estimation experiment also revealed that this model can fill in human motion information in high frame rate scenes. |
| تدمد: | 1993-5013 1673-1905 |
| DOI: | 10.1007/s11801-021-0201-8 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::59893af95d04d467a532a50e491306e7 https://doi.org/10.1007/s11801-021-0201-8 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi...........59893af95d04d467a532a50e491306e7 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19935013 16731905 |
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| DOI: | 10.1007/s11801-021-0201-8 |