Image Super-Resolution Using VDSR-ResNeXt and SRCGAN
This work addresses image quality enhancement for computer vision applications, but appears incremental as it builds on existing VDSR and GAN methods.
The paper tackled image super-resolution by proposing VDSR-ResNeXt, a deep multi-branch convolutional network, and SRCGAN, a conditional GAN using class labels, achieving results assessed on benchmark datasets.
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.