  Over the past decade, many Super Resolution techniques have been developed
using deep learning. Among those, generative adversarial networks (GAN) and
very deep convolutional networks (VDSR) have shown promising results in terms
of HR image quality and computational speed. In this paper, we propose two
approaches based on these two algorithms: VDSR-ResNeXt, which is a deep
multi-branch convolutional network inspired by VDSR and ResNeXt; and SRCGAN,
which is a conditional GAN that explicitly passes class labels as input to the
GAN. The two methods were implemented on common SR benchmark datasets for both
quantitative and qualitative assessment.
