RETRACTED ARTICLE: Application of single frame image super-resolution algorithm based on generative adversarial network in tennis motion image resolution

Abstract

Researchers who have successfully applied convolutional neural networks in the field of computer vision have developed a series of image super-resolution algorithms based on convolutional neural networks. In this way, the effect of image reconstruction is greatly improved. The existing image super-resolution algorithm realizes the ideal mathematical modeling of the image degradation process, and obtains a low-resolution image through a properly modeled degradation process. Then, a pair of high-resolution images and low-resolution images is used for surveillance-related training. As an effective software processing technology, image super-resolution reconstruction can break the physical boundaries of image equipment and improve the spatial resolution of the acquired image. This thesis focuses on the research of image emphasis and super-resolution reconstruction based on space animation, especially the image processing of the position and docking of spacecraft. Aiming at the characteristics of spatial moving images and the problems of application programs such as edge patterns, noise effects, and real-time processing, the algorithm is studied. We propose an image enhancement and super-resolution reconstruction algorithm based on multi-scale conversion. This research and design method once again realizes the image design and reconstruction.