Image Zooming Algorithms using Total Variation
K. Nakao, Yoshiharu Koya, Y. Kubo, S. Sugimoto · Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications · 2016
In this paper, we consider the algorithms for minimizing the total variation norm of an image for zooming a single image. Super-resolution imaging is a technique to reconstruct a high-resolution image from a low-resolution image. To obtain a high-resolution image that preserves edges and has no ringing, we choose the image which minimizes the total variation (TV) norm of the image with constraints, according to Chambolle's method. This regularization based method is compared with some traditional methods. On the other hand, a total variation based method has huge computational cost because of it's iterative scheme. We also compare processing times in this paper.