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Image Super-Resolution Using Deep Belief Networks

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Published:10 July 2014Publication History

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.

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    • Published in

      cover image ACM Other conferences
      ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service
      July 2014
      430 pages
      ISBN:9781450328104
      DOI:10.1145/2632856

      Copyright © 2014 ACM

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      Publication History

      • Published: 10 July 2014

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      Overall Acceptance Rate163of456submissions,36%

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