Single Image Super-Resolution Methods: A Survey
It provides a comprehensive overview for researchers in signal and image processing, but is incremental as it synthesizes existing work.
This survey paper compares different single image super-resolution models, examining their evolution and diverse objectives over the years, without presenting new experimental results.
Super-resolution (SR), the process of obtaining high-resolution images from one or more low-resolution observations of the same scene, has been a very popular topic of research in the last few decades in both signal processing and image processing areas. Due to the recent developments in Convolutional Neural Networks, the popularity of SR algorithms has skyrocketed as the barrier of entry has been lowered significantly. Recently, this popularity has spread into video processing areas to the lengths of developing SR models that work in real-time. In this paper, we compare different SR models that specialize in single image processing and will take a glance at how they evolved to take on many different objectives and shapes over the years.