Image Super-Resolution Reconstruction in Sports Scenarios and Its Application in Motion Analysis

Abstract

With the rapid development of sports technology, the demand for high-definition images in sports competition analysis has been increasing. Particularly in fast-paced sports such as basketball, traditional image capture technology often fails to provide sufficient detail resolution, limiting in-depth analysis of athletic techniques and tactical layouts. To address this, image super-resolution reconstruction technology has been extensively studied and applied to enhance image quality, thereby providing coaches and analysts with clearer visual materials. However, existing super-resolution methods mainly focus on static images and struggle to overcome the challenges of blurring and real-time processing demands in motion scenarios. This paper introduces a dynamic adaptive cascaded network-based method for super-resolution reconstruction of images in motion scenarios, combined with dynamic 3D motion scene imaging techniques, aimed at enhancing the accuracy and timeliness of motion analysis. Through these innovative methods, not only can image degradation caused by motion be effectively handled, but higher-dimensional data support can also be provided for motion analysis.