@inproceedings{eb66d2561fad4bb29f072d52c9c903bf,
title = "Image super-resolution using deep belief networks",
abstract = "In this paper, we aim at using Deep Belief Networks (DBNs) to solve the problem of image super-resolution (SR). We exploit the hierarchical structure of the DBNs to capture the non-linear mapping from low-resolution (LR) patches to their high-resolution (HR) counterpart. When a query LR image is input, we divide it into a list of patches, then we put each patch into a forward propagation network which is a trained deep belief network. The output is the predicted HR patches. Finally, we combine the HR patches into expected HR images. We evaluate our approach on a popular dataset which is used in other super-resolution literature. Experimental results demonstrate the performance of our method is superior to several state-of-the-art super-resolution methods both quantitatively and perceptually.",
keywords = "Deep belief networks, Super-resolution",
author = "Yanwen Zhou and Yanyun Qu and Yuan Xie and Wensheng Zhang",
year = "2014",
doi = "10.1145/2632856.2632915",
language = "英语",
isbn = "9781450328104",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "28--31",
booktitle = "ICIMCS 2014 - Proceedings of the 6th International Conference on Internet Multimedia Computing and Service",
address = "美国",
note = "6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014 ; Conference date: 10-07-2014 Through 12-07-2014",
}