Semi-supervised student-teacher learning for single image super-resolution

Highlights：

• We propose a deep learning-based semi-supervised approach for SISR. We build our framework via adversarial learning.

• To better facilitate the learning of unlabelled data, we propose a student-teacher (S-T) model to transfer the knowledge from supervised learning (teacher) to the unsupervised learning (student). The S-T model is based on partial weight-sharing of dual discriminators and a pair matching network playing two roles: cycle consistency and ‘latent discriminator’ for better learning of unlabelled data.

• We propose a new SR network structure to better learn the non-local features from LR images via channel in channel and spatial in spatial mechanisms.

• We demonstrate that our method outperforms some purely supervised and unsupervised methods on various experimental settings.