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Hybrid Example-Based Single Image Super-Resolution

  • Yang XianEmail author
  • Xiaodong Yang
  • Yingli Tian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)

Abstract

Image super-resolution aims to recover a visually pleasing high resolution image from one or multiple low resolution images. It plays an essential role in a variety of real-world applications. In this paper, we propose a novel hybrid example-based single image super-resolution approach which integrates learning from both external and internal exemplars. Given an input image, a proxy image with the same resolution as the target high-resolution image is first generated from a set of externally-learnt regression models. We then perform a coarse-to-fine gradient-level self-refinement on the proxy image guided by the input image. Finally, the refined high-resolution gradients are fed into a uniform energy function to recover the final output. Extensive experiments demonstrate that our framework outperforms the recent state-of-the-art single image super-resolution approaches both quantitatively and qualitatively.

Keywords

Input Image Gaussian Mixture Model Gaussian Component Ground Truth Image External Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported in part by ONR grant N000141310450 and NSF grants EFRI-1137172, IIP-1343402.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.The Graduate CenterThe City University of New YorkNew YorkUSA
  2. 2.NVIDIA ResearchSanta ClaraUSA
  3. 3.The City CollegeThe City University of New YorkNew YorkUSA

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