A trio of AI researchers at KAIST AI, in Korea, has developed what they call a Chain-of-Zoom framework that allows the generation of extreme super-resolution imagery using existing super-resolution models without the need for retraining.
In their study published on the arXiv preprint server, Bryan Sangwoo Kim, Jeongsol Kim, and Jong Chul Ye broke down the process of zooming in on an image and then used an existing super-resolution model at each step to refine the image, resulting in incremental improvements in resolution.
The team in Korea began by noting that existing frameworks for improving the resolution of pictures tend to use interpolation or regression when zooming, resulting in blurry imagery. To overcome these problems, they took a new approach—using a stepwise zooming process, in which subsequent steps improve on those that came before.