CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Motion based image deblur using recurrent neural network for power transmission line inspection robot
Siyao Fu; Yunchu Zhang; Long Cheng; Zize Liang; Zeng-Guang Hou; Min Tan
2006
Conference NameIEEE International Joint Conference on Neural Network
Conference DateJUL 16-21, 2006
Conference PlaceVancouver
CountryCanada
AbstractHigh-voltage power transmission line inspection robot must plan its behavior to detect the obstacles from the complex background according to their types when it is crawling along the power transmission line in order to negotiate reliably. In most cases, robot fulfills the task by its vision system. However, motion blur due to camera motion caused by wind or other unknown causes can significantly degrade the quality of the image acquired. This is a typical kind of the so called image restoration problem, which is a hard problem since no prior knowledge of the motion is available. For this purpose, a novel approach for image restoration is proposed. The restoration procedure consists of two stages: estimation of blur function parameters and reconstruction of images. Image degradation model is proposed first to identify blur function parameters, then a recurrent neural network is used to restore the blurred image. Experiments on real blurred images on power transmission line prove the feasibility and reliability of this algorithm. Our experiments show that the restoration procedure consumes only small amount of computation time.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23164
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
Siyao Fu,Yunchu Zhang,Long Cheng,et al. Motion based image deblur using recurrent neural network for power transmission line inspection robot[C],2006.
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