A deep learning-based super-resolution method for educational images and an analysis of its educational effectiveness

Abstract

As the digital transformation of education continues to advance, instructional images serve as the core visual medium for knowledge transfer, with their clarity and level of detail directly influencing teaching outcomes. However, a vast amount of historical educational resources suffer from issues such as low resolution and blurred textures due to limitations in the capture equipment. The super-resolution technology of images aims to reconstruct high-resolution details from low resolution images. In recent years, deep learning methods have achieved breakthrough progress in this field. This paper systematically traces the development of deep learning-based super-resolution technology, from the evolution of CNN based models and GAN generative models to Transformer architectures, with a focus on analysing the technical characteristics and strengths and weaknesses of representative models at each stage. Building on this, it reviews the progress of applied research on super-resolution technology in the education sector, paying particular attention to the emerging research direction of evaluating teaching effectiveness. Research indicates that deep learning-based super resolution techniques can significantly enhance the images quality and information readability of educational images, leading to a marked improvement in knowledge transfer efficiency. However, current research still faces challenges such as insufficient adaptation to teaching scenarios, difficulties in lightweight deployment, and an incomplete evaluation framework for effectiveness. This paper aims to provide a systematic theoretical reference for researchers in the field of educational technology, thereby promoting the effective application of super-resolution techniques in teaching practice.