Image Motion Blur Removal Algorithm Based on Motion Offset Information Estimation
In response to the problem that existing motion blur removal algorithms for dynamic scene images are difficult to ef-fectively restore non-uniform composite motion blur,a new motion offset estimation framework is proposed by introducing camer-a exposure trajectories to represent the motion information contained in blurred images.This framework is used to model pixel motion offsets of latent clear images at multiple discrete time points.A multi-scale single image deblurring network framework integrating pixel motion offset information is proposed based on the estimated motion offset information.This framework fuses motion offset information during the decoding stage through deformable convolution,giving each pixel different motion con-straints.Through the multi-scale encoding and decoding structure of the network,the predicted values of each pixel on each scale are obtained,achieving end-to-end blur image restoration.The experimental results on GoPro and HIDE datasets show that this algorithm can effectively improve image quality,with an average increase of 1.9 dB in peak signal-to-noise ratio and 0.03 in structural similarity.
万方数据
维普
