A Fully Progressive Approach to Single-Image Super-Resolution

Understand

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.
• To this end, we propose a method (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.