Earticle

초록

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.

목차

Abstract
1. Introduction
2. Super Resolution using Convolutional Neural Networks
3. Super Resolution using Generative Adversarial Networks
4. Super Resolution using Transformer
5. Comparison and Evaluation
6. Conclusion
Acknowledgement
References

저자

• Astha Adhikari [ Department of AI Software Gachon University ]
• Sang-Woong Lee [ Department of AI Software Gachon University ] Corresponding author

참고문헌