@inproceedings{dcf31bfa6d6d421e9428b0037002a0c2,
title = "Self-tuned deep super resolution",
abstract = "Deep learning has been successfully applied to image super resolution (SR). In this paper, we propose a deep joint super resolution (DJSR) model to exploit both external and self similarities for SR. A Stacked Denoising Convolutional Auto Encoder (SDCAE) is first pre-trained on external examples with proper data augmentations. It is then fine-tuned with multi-scale self examples from each input, where the reliability of self examples is explicitly taken into account. We also enhance the model performance by sub-model training and selection. The DJSR model is extensively evaluated and compared with state-of-the-arts, and show noticeable performance improvements both quantitatively and perceptually on a wide range of images.",
keywords = "Adaptation models, Convolutional codes, Image resolution, Joints, Pediatrics, Training, Yttrium",
author = "Zhangyang Wang and Yingzhen Yang and Zhaowen Wang and Shiyu Chang and Wei Han and Jianchao Yang and Thomas Huang",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015 ; Conference date: 07-06-2015 Through 12-06-2015",
year = "2015",
month = oct,
day = "19",
doi = "10.1109/CVPRW.2015.7301266",
language = "English (US)",
series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",
publisher = "IEEE Computer Society",
pages = "1--8",
booktitle = "2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015",
}