AIGC驱动的图像超分重构赋能教学实践应用研究OA

With the advancement of Artificial Intelligence Generated Content(AIGC)technology,the application of images in educational settings has emerged as a new research focus.As a pivotal medium for knowledge transmis-sion,the clarity,texture details,color vibrancy,and overall image fidelity of images directly influence teaching effi-cacy.This study aims to modify the architecture of the diffusion model to achieve Super-Resolution(SR)recon-struction of images suffering from various quality degradation issues,and to evaluate the effectiveness of applying these SR-enhanced images across diverse teaching contexts.Initially,the study addresses the mismatch between im-age quality and teaching requirements by refining the diffusion model's structural design.Subsequently,both subjec-tive and objective experiments are conducted to integrate SR-reconstructed images into real-world teaching environ-ments.Finally,a comprehensive evaluation framework is constructed based on the experimental findings to substanti-ate the practical benefits of the reconstructed images.The results show that compared to images generated by tradi-tional methods,the application of SR images generated by the modified model in teaching activities improves the av-erage efficiency of knowledge transfer by approximately 22.9%,and reduces the time teachers spend on lesson prep-aration by about 15.6%.This study provides a theoretical foundation and practical insights for leveraging artificial intelligence to drive pedagogical innovation.