Soccer Video Super-Resolution via Sub-Pixel Convolutional Neural Network

In this paper, we consider the problem of the soccer video super-resolution (SR). We propose an end-to-end framework based on the sub-pixel convolution neural network and pre-train our network on the image datasets to improve the resolution of soccer video and the speed of the SR algorithm. Different from the general SR methods, our method does not need to interpolate the low-resolution (LR) images first. It means that we directly extract the features from the LR images so the computational cost is low. We also compare our proposed algorithm with the current SR methods, and the experimental results show that our network has better performance. In order to evaluate the effectiveness of SR, we apply the proposed method to the object detection on the soccer videos. The experiments show that integrating the SR technique into the detecting field can improve the accuracy of detection.