Image Super-Resolution Using VDSR-ResNeXt and SRCGAN

Image super-resolution (SR) is a computer vision task involving reconstruction of high-resolution (HR) images given a low-resolution (LR) image as input. It is highly challenging and has many practical applications, such as medical image processing [22], satellite imaging [6], facial image enhancement [17], [18] and compressed image improvement [16]. Over the past decade, many SR techniques have been developed using deep learning. Among those, generative adversarial networks (GAN) [4] and very deep convolutional networks (VDSR) [7] 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.