Single Image Super-Resolution
This paper provides a review for researchers interested in the field of single image super-resolution, offering an incremental overview of existing methods.
This paper provides a chronological overview of single image super-resolution, defining the problem, challenges, and performance metrics. It reviews reconstruction-based and deep learning approaches, quantitatively comparing three landmark architectures and noting that the latest network outperforms previous methods.
This study presents a chronological overview of the single image super-resolution problem. We first define the problem thoroughly and mention some of the serious challenges. Then the problem formulation and the performance metrics are defined. We give an overview of the previous methods relying on reconstruction based solutions and then continue with the deep learning approaches. We pick 3 landmark architectures and present their results quantitatively. We see that the latest proposed network gives favorable output compared to the previous methods.