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.
@cite_12: We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.
There is a vast amount of literature on single image super-resolution using deep learning techniques. @cite_12 is one of the pioneers that demonstrated the potential for convolutional networks to be applied to this research domain, achieving then state-of-art reconstruction quality while maintaining a lightweight structure. The model consists of 3 convolutional layers, each responsible for feature extraction, non-linear mapping to high-resolution patch representations and reconstruction, respectively. Although SRCNN learns an end-to-end mapping between LR and HR images, it is still a fairly shallow network by current standards, and superior performances have been reported with deeper models and other CNN architecture variants. Moreover, since SRCNN can be trained only on a single scale of magnification, it is constrained to work only on the specific scale on which it has been trained. This led us to consider other techniques.