To solve the problem of motion blur in abnormal behavior detection, a fast motion blur removal algorithm, based on DeblurGAN, is proposed. Three 3×3 convolutions are used to replace the 7×7 convolution in the original generator. The transposed convolution is discarded. Firstly, bilinear interpolation is used to expand the size of the feature map which needs upsampling. The residual unit is replaced by a residual density block (RRDB) in the original algorithm. The RRDB is then scaled to 0~1 to avoid unstable training. The L1 loss of gradient images is added to the loss function of the original generator. As the DeblurGAN reconstructed image edge is often not clear enough, the edge information of the image is added to make the reconstructed image edge more obvious. The effectiveness of this method is verified by experiments and is compared with other similar algorithms like DeblurGAN. The PSNR of the optimized model is improved by 0.94. The structure similarity and speed are equivalent. The chessboard lattice problem in the reconstructed image is solved. The edge of detail is more prominent. The performance of the proposed model is better than that of other related algorithms.