No abstract

In sports science research, the dynamic non-uniform blur caused by the movement of running athletes is a challenging problem in computer vision, that seriously affects the judgment accuracy of the finish-line photography system. With the rapid development of deep learning technology, image preprocessing, object identification, and object classification have been widely used and studied. This work proposes multi-scale convolutional neural network image deblurring to eliminate dynamic blur generated by the athletes in the shooting process. This network comprises three end-to-end convolutional neural subnetworks of different scales to recover the blurry athlete image caused by various factors on the field. The system effectively extracts the detailed edge of the image on each scale from coarse to fine. Many experiments show that this method can deblur the image captured by the finish-line photography system in real-time and rapidly achieve a better visual effect in the athlete’s dynamic image.