Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network

doi:10.1007/s11633-022-1350-3

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network
	Si-Qi Li 2,3,4,5
刊名	Machine Intelligence Research
	2022
卷号	19 页码:307-318
关键词	Event camera multi-modal fusion image de-occlusion spiking neural network (SNN) image reconstruction
ISSN号	2731-538X
DOI	10.1007/s11633-022-1350-3
英文摘要	Seeing through dense occlusions and reconstructing scene images is an important but challenging task. Traditional frame based image de-occlusion methods may lead to fatal errors when facing extremely dense occlusions due to the lack of valid information available from the limited input occluded frames. Event cameras are bio-inspired vision sensors that record the brightness changes at each pixel asynchronously with high temporal resolution. However, synthesizing images solely from event streams is ill-posed since only the brightness changes are recorded in the event stream, and the initial brightness is unknown. In this paper, we propose an event-en hanced multi-modal fusion hybrid network for image de-occlusion, which uses event streams to provide complete scene information and frames to provide color and texture information. An event stream encoder based on the spiking neural network (SNN) is proposed to en code and denoise the event stream efficiently. A comparison loss is proposed to generate clearer results. Experimental results on a large scale event-based and frame-based image de-occlusion dataset demonstrate that our proposed method achieves state-of-the-art performance.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49644]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.Department of Automation, Tsinghua University, Beijing 100084, China 2.Key Laboratory for Information System Security, School of Software, Tsinghua University, Beijing 100084, China 3.Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Tsinghua University, Beijing 100084, China 4.Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China 5.Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
推荐引用方式 GB/T 7714	Si-Qi Li. Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network[J]. Machine Intelligence Research,2022,19:307-318.
APA	Si-Qi Li.(2022).Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network.Machine Intelligence Research,19,307-318.
MLA	Si-Qi Li."Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network".Machine Intelligence Research 19(2022):307-318.