earticle

영어

Photo-realistic high-resolution image reconstruction from its counterpart low-resolution image is still a long, challenging task in computer vision. Estimating a High Resolution (HR) image from a single Low Resolution (LR) is referred to as Super-Resolution (SR). When deep learning with a learning and noise immunity capability has been applied, the LR images are generally obtained by down-sampling HR images with additional noise and blur. Single Image Super-Resolution (SISR) is more respected and better in efficiency. Based on models and architectures, deep learning super-resolution techniques can be categorized into Convolutional Neural Networks (CNNs), Adversarial Networks, and Transformer models. This paper summarizes the development history and characteristics of the state-of-the-art papers, discusses the trend and challenges, and provides a compact review of current development and deep learning-based super resolution trends.