@inproceedings{9a9cd7ee99a64264a9bfea2cfaef5f61,
title = "Texture-centralized deep convolutional neural network for single image super resolution",
abstract = "There have been significant progresses in single image super-resolution (SR) using deep convolutional neural network. In this paper, we propose a modified deep convolutional neural network model incorporated with image texture priors for single image SR. The model consist of a particular feature extraction layer followed by image reconstruction process, aiming to centralize on the image texture information so as to make the overall SR task more effective. This proposal is compared with current state-of-The-Art methods on standard images. Our experimental results confirmed that, incorporating image texture prior information with conventional high-resolution image reconstruction process can lead to better performance and faster convergence speed simultaneously.",
keywords = "Super-resolution, deep convolutional neural network, image texture prior",
author = "Chengqi Li and Zhigang Ren and Bo Yang and Xingyu Wan and Jinjun Wang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 Chinese Automation Congress, CAC 2017 ; Conference date: 20-10-2017 Through 22-10-2017",
year = "2017",
month = dec,
day = "29",
doi = "10.1109/CAC.2017.8243424",
language = "英语",
series = "Proceedings - 2017 Chinese Automation Congress, CAC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3707--3710",
booktitle = "Proceedings - 2017 Chinese Automation Congress, CAC 2017",
}