Simulation of Deblurring Network for Multilayer Motion Image Restoration Algorithm
Multi-layer motion images have poor detail texture due to blurring,which can not effectively transmit information.In order to solve the above problems,based on the AED aerobics data set and digital image processing,this paper constructs an image restoration algorithm which combines deblurring network with moving object detection,namely DGV2-YOL3 algorithm.Firstly,the AED data samples were intercepted and preprocessed,and the image fea-ture extraction ability was improved by using the down-sampling method;Then the image features were collected based on four groups of channels,the number of system parameters is reduced by 25%,and the residual learning of the features is carried out by using a fine-grained module;and then the learning features are fused,and an image was reconstructed by using a global jump method;Finally,based on the DBSACAN algorithm,the image anchor point and IOU threshold were adaptively planned to identify and detect the motion type and improve the detection accuracy.The subjective analysis of simulation experiments shows that the image processed by DGV2-YOL3 algorithm has clear tex-ture,prominent edges and obvious recovery of″socks″details compared with other algorithms;The objective analysis re-sults show that DGV2-YOL3 algorithm has the highest peak signal to noise ratio(PSNR)and classification index,which are improved by 13.48%and 5.01%respectively compared with the other eight stacking algorithms,and the data process-ing efficiency ranks third,which has high real-time performance.To sum up,the DGV2-YOL3 algorithm effectively improves the edge detail texture restoration ability of the blurred image,and has high simulation research value.
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