Video Super-Resolution Using Plug-and-Play Priors

Video super-resolution is a fundamental task in computer vision, aiming to enhance the resolution and visual quality of low-resolution videos. Plug-and-Play Priors is one of the most widely used frameworks for solving computational imaging problems by integrating physical and learned models. Traditional approaches often rely on handcrafted priors, which are computationally expensive and may not generalize well to diverse video content. In this paper, we propose a novel approach for video super-resolution using Plug-and-Play Priors with motion estimation. By leveraging the power of deep learning and the flexibility of the Plug-and-Play framework, our method achieves promising results while maintaining computational efficiency. Experimental results on benchmark datasets demonstrate the superiority of our approach in terms of both quantitative metrics and visual quality.