Variational single image interpolation with time-varying regularization

Single image interpolation has wide applications in digital photography and image display. Most single image interpolation approaches achieve state-of-the-art performance at the expense of very high computation time. While efficient alternatives exist, they do not reach the same level of image quality. In this paper, we propose an image interpolation method offering both high computational efficiency and high interpolation quality. We exploit a newly-developed variational framework with time-varying regularization, i.e., the parameters of the regularization are allowed to change with time, making it different to conventional variational problems with time-independent regularization parameters. These time-varying parameters are learned from training samples. We train the model parameters for the problem of single image interpolation. Experiments show that the trained models lead to promising quality of the interpolated images in terms of quantitative measurements (e.g., PSNR and SSIM), compared with the state-of-the-art approaches. Meanwhile, high computational efficiency is obtained.