Optical super-resolution imaging based on improved planning algorithm in sports teaching simulation

With the advancement of network technology, online physical education has emerged as a superior teaching approach compared to traditional physical education, as it better caters to students' learning requirements. However, the capability of data collection and analysis in online physical education in China remains relatively limited. Due to the constraints of optical imaging technology, low-resolution images fail to provide precise movement information, making it challenging to promptly assess and provide feedback on students' learning progress. This study aims to utilize an enhanced planning algorithm to achieve optical super-resolution imaging, thereby enhancing the accuracy and visualization of motion data in physical education. The research collects a series of low-resolution moving images as input data and devises an improved planning algorithm to reconstruct these images in super-resolution. By integrating knowledge from the optical field and optimization technology, the algorithm effectively enhances image resolution and sharpness. Experimental results demonstrate the significant performance improvement achieved by the proposed improved planning algorithm in optical super-resolution imaging. Compared to conventional methods, this algorithm produces motion images of higher resolution and clarity, thereby enhancing the visualization of motion data.