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.
@article{arxiv.1504.05632,
title = {Self-Tuned Deep Super Resolution},
author = {Zhangyang Wang and Yingzhen Yang and Zhaowen Wang and Shiyu Chang and Wei Han and Jianchao Yang and Thomas S. Huang},
journal= {arXiv preprint arXiv:1504.05632},
year = {2015}
}