Image super-resolution: The techniques, applications, and future

By Linwei Yue, Huanfeng Shen, Jie Li, Qiangqiang Yuan, Hongyan Zhang, Liangpei Zhang. 2016

537 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Arvid Lundervold
2. Zhenwei Shi
3. Zhengxia Zou
4. Sen Lei

In Media Technology

1. Arvid Lundervold
2. Zhenwei Shi
3. Zhengxia Zou
4. Sen Lei
5. Sheng Fang
6. Zhe Li
7. Kaiyu Li

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, background): “Image super-resolution, reconstructing a higher-resolution image or image sequence from the observed low-resolution image [190], is an exciting application of deep learning methods.”
• Super-Resolution for Remote Sensing Images via Local–Global Combined Network (2017, background): “Instead of devoting to physical imaging technology, many researchers aim to recover high-resolution images from low-resolution ones using an image processing technology called super-resolution [1].”
• Real-World Single Image Super-Resolution: A Brief Review (2021, unknown): “For example, Yue et al. [124] make a summary of the techniques, applications and future of image SR.”
• Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks (2018, unknown): “While blurring and down-sampling are commonly considered for image super-resolution [33], in the context of image recognition they have a relatively lower impact.”