Online oil painting teaching based on deep learning and image super-resolution reconstruction

Despite advances in digital education making online oil painting lessons feasible, clarity and accuracy are still issues. Videos of low resolution or inadequate clarity make brushwork and texture, two essential elements of oil painting, difficult to discern. This work suggests an innovative deep learning-based online oil painting teaching framework (IDL-OOPTF) with picture super-resolution reconstruction to address this problem. The technique enhances real-time painting demos and student participation by utilizing a Convolutional Neural Network (CNN) model for semantic scene interpretation and an enhanced super-resolution generative adversarial network (SRGAN). Because of this invention, students can see finer details and color gradients in low-resolution television broadcasts. The proposed model outperforms baseline SR techniques and traditional interpolation on PSNR, SSIM, and customer satisfaction measures using a carefully chosen collection of oil painting pictures and instructional videos. Notable results include 35% more student engagement and 28% more detail recognition. Ultimately, this method enhances the pedagogical and aesthetic quality of online oil painting classes, addressing the issue of distant art education on a large scale. Customization using reinforcement learning and adaptive feedback are being developed.