主题：A Fully Progressive Approach to Single-Image Super-Resolution
Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each case, it remains challenging to achieve high-quality results for large upsampling factors. In this talk, I'll introduce our work named ProSR that is progressive both in architecture and training: the network upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning. To obtain more photorealistic results, we design a generative adversarial network (GAN), named ProGanSR, that follows the same progressive multi-scale design principle. ProSR ranks 2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge. Compared to the top-ranking team, our model is marginally lower but runs 5 times faster.
Yifan Wang is a PhD student in the Interactive Geometry Lab @ ETH Zurich and the Imaging and Video group @ Disney Research Zurich supervised by Prof. Olga Sorkine-Hornung. Her research interest includes machine learning, image processing, and geometry processing.